PHP is one of the most widely used server-side scripting languages, driving a significant portion of the web. As websites grow in complexity and traffic, optimizing PHP for performance and scalability becomes essential. PHP FPM (FastCGI Process Manager), is a tool designed to enhance PHP's efficiency. In this blog, we will explore what PHP FPM is, how it works,  and why it is essential for modern web applications.

Table of Contents:

1. Understanding PHP FPM
2. Key features of PHP FPM
3. How PHP FPM works?
4. Benefits of PHP FPM
5. Use cases for PHP FPM
6. Getting started with PHP FPM
7. Most used PHP FPM configurations
8. Alternatives to PHP FPM
9. Atatus PHP monitoring

Understanding PHP FPM

PHP FPM is an alternative PHP FastCGI implementation designed for handling high-performance applications. It works as a process manager for PHP scripts, allowing web servers to execute PHP efficiently.

Unlike traditional PHP setups, where PHP runs as a module within the web server, PHP FPM operates independently. It creates a pool of worker processes that execute PHP scripts and uses the FastCGI protocol to communicate with the web server.

Key features of PHP FPM

1. Process management: PHP FPM manages multiple worker processes, ensuring efficient script execution without overloading the server.
2. Performance optimization: By maintaining a pool of pre-spawned processes, PHP FPM reduces the overhead of starting and stopping processes for every request.
3. Scalability: PHP FPM is highly configurable, allowing adjustments to suit low-traffic sites and high-demand enterprise applications.
4. Improved reliability: Features like request timeouts and process limits help prevent server crashes caused by poorly written scripts or unexpected spikes in traffic.

How PHP FPM works?

PHP FPM improves the performance of PHP applications by managing how web servers like NGINX, Apache, or Lighttpd interact with PHP scripts. It uses the FastCGI protocol to handle requests and assigns them to worker processes in an efficient way. Let’s break down how PHP FPM works.

The FastCGI Protocol

PHP FPM communicates with web servers like NGINX, Apache, or Lighttpd using the FastCGI protocol. FastCGI acts as a middleman, passing requests from the web server to PHP FPM and sending back the output to the client.

Process Pooling

When a request comes in, PHP FPM assigns it to an available worker process from its pool. This pool operates under one of the following modes:

1. Static: A fixed number of worker processes are always running.
2. Dynamic: The number of processes adjusts based on traffic demand.
3. On-Demand: Processes are created and terminated as needed, minimizing idle resource usage.

Example integration with NGINX:

location ~ \.php$ {
    include fastcgi_params;
    fastcgi_pass 127.0.0.1:9000;
    fastcgi_param SCRIPT_FILENAME $document_root$fastcgi_script_name;
}

Benefits of PHP FPM

1. Enhanced performance:  PHP FPM pre-spawns worker processes, reducing the overhead of creating a new process for each request. This makes it ideal for high-traffic websites.
2. Resource management: Features like pm.max_children (maximum number of processes) and pm.max_requests (requests per process before respawning) allow fine-grained control over resource allocation.
3. Improved fault tolerance: PHP FPM can terminate processes that exceed predefined limits, such as execution time or memory usage, preventing a single request from impacting overall server performance.
4. Better integration with web servers: PHP FPM works seamlessly with web servers like NGINX and Apache, enabling modern setups with improved load balancing and caching capabilities.
5. Secure multi-tenancy: PHP FPM supports running worker processes under different user accounts, isolating applications and enhancing security in shared hosting environments.

Use cases for PHP FPM

1. High-traffic websites: E-commerce platforms, news websites, and large-scale applications benefit from PHP FPM's ability to handle multiple concurrent requests efficiently.
2. Shared hosting environments: Hosting providers use PHP FPM to isolate users and allocate resources fairly, ensuring one user doesn't affect another.
3. Performance-critical applications: Applications requiring low latency and high reliability, such as APIs and real-time platforms, leverage PHP FPM for optimized performance.

Getting started with PHP FPM

1. Installation

Most Linux distributions provide PHP FPM packages. For example, on Ubuntu:

sudo apt install php-fpm

2. Configuration

The main configuration file (php-fpm.conf) and pool-specific files (/etc/php/7.x/fpm/pool.d/*.conf) allow you to tailor PHP FPM settings.

Key parameters include:

• pm (static, dynamic, or ondemand)
• pm.max_children (maximum worker processes)
• request_terminate_timeout (maximum script execution time)

3. Integration with Web Servers

For NGINX, configure the fastcgi_pass directive to point to PHP FPM:

location ~ \.php$ {
    include fastcgi_params;
    fastcgi_pass 127.0.0.1:9000;
    fastcgi_param SCRIPT_FILENAME $document_root$fastcgi_script_name;
}

Restart both the web server and PHP FPM after configuration changes:

sudo systemctl restart php-fpm
sudo systemctl restart nginx

Most used PHP FPM configurations

Here are some commonly used PHP FPM configurations, their roles, and their impact on performance and resource management:

1. pm (Process Manager):

The pm directive defines the process management mode for PHP-FPM, determining how worker processes are managed.

Options:

• static: A fixed number of child processes are created and remain active.
• dynamic: Adjusts the number of child processes based on traffic demands.
• ondemand: Processes are spawned only when needed and terminated after a defined idle time.

Use Cases:

• Use static for predictable, high-traffic workloads where you need consistent resource allocation.
• Use dynamic for environments with variable traffic.
• Use ondemand for low-traffic or resource-constrained servers.

2. pm.max_children:

The pm.max_children defines the maximum number of child processes that can be created to handle requests. This setting directly impacts the number of concurrent requests the server can manage. Setting this too high may deplete server memory, while a lower value might cause request delays. The default value typically ranges between 5 and 10, depending on the distribution.

3. pm.start_servers, pm.min_spare_servers, pm.max_spare_servers:

These directives apply when pm is set to dynamic and help manage worker processes during varying traffic conditions.

• pm.start_servers: Number of child processes to start initially.
• pm.min_spare_servers: Minimum number of idle worker processes to maintain.
• pm.max_spare_servers: Maximum number of idle worker processes allowed.

4. pm.max_requests:

Specifies how many requests a worker process can handle before it is respawned. It helps mitigate memory leaks from third-party libraries. The default value is typically set to 500.

5. request_terminate_timeout:

Sets the maximum execution time for a single request.If a request exceeds this limit, the corresponding process is terminated, preventing long-running scripts from consuming resources. The default value is 0, which means it is disabled.

6. listen:

Configures the address (UNIX socket or IP:port) where PHP FPM listens for FastCGI requests.

Example:

listen = 127.0.0.1:9000
listen = /var/run/php/php7.4-fpm.sock

7. user and group:

The user and group settings specify the user and group under which PHP-FPM worker processes operate. This enhances security by isolating the processes and preventing unauthorized access to server resources.

8. access.log, slowlog

The access.log file logs all requests processed by PHP-FPM, providing valuable insights for performance monitoring and debugging. The slowlog records details of slow requests, helping to identify bottlenecks or problematic scripts that may require optimization. These logs are crucial for maintaining server efficiency and improving performance.

Alternatives to PHP FPM

While PHP FPM is a robust and widely adopted solution, there are alternatives to consider based on specific requirements:

1. Apache mod_php

Runs PHP as an Apache module, allowing PHP to be executed directly within the web server.

Pros:

• Simple to configure.
• Tight integration with Apache.

Cons:

• Less efficient for handling concurrent connections.
• Tied to Apache, limiting web server flexibility.

Use Case: Suitable for smaller applications where simplicity is a priority.

2. HHVM (HipHop Virtual Machine)

An alternative PHP runtime developed by Meta (formerly Facebook). It supports PHP and Hack.

Pros:

• Extremely fast for specific workloads.
• Designed for high-performance environments.

Cons:

• Limited support for newer PHP features and versions.
• Smaller community and ecosystem compared to PHP FPM.

Use Case: Large-scale applications requiring maximum performance.

3. CGI and FastCGI

CGI is a traditional method to execute scripts, while FastCGI is an optimized version that reuses processes.

Pros:

• Simple to set up.
• Works with various web servers.

Cons:

• CGI is slow due to the process-per-request model.
• FastCGI lacks some advanced features of PHP FPM.

Use Case: Legacy setups or environments requiring simplicity.

4. uWSGI

A versatile application server that can run PHP scripts.

Pros:

• Supports multiple languages.
• Highly configurable.

Cons:

• More complex setup compared to PHP FPM.
• Larger memory footprint.

Use Case: Multi-language applications requiring advanced features.

5. Node.js or Other Language Stacks

For some applications, replacing PHP entirely with Node.js, Python, or Go may offer better performance or scalability.

Pros:

• Modern, event-driven architectures.
• Rich ecosystem.

Cons:

• Requires rewriting applications.
• May not be ideal for legacy PHP projects.

Use Case: New projects with flexibility in choosing technologies.

Atatus PHP FPM monitoring: Enhance Your Application's Performance Effortlessly

Atatus PHP FPM Monitoring offers a robust solution for monitoring and enhancing the performance of your PHP FPM applications. It provides real-time insights into server performance, identifies bottlenecks, and ensures smooth operation of your web applications.

Key Features of Atatus PHP FPM Monitoring

• Detailed PHP FPM metrics: Monitor key metrics like process utilization, request rates, idle processes, and memory usage to ensure your PHP FPM pools are performing optimally.
• Worker process insights: Track the performance of each worker process in the pool, including request handling times, process spawning, and termination events.
• Slow request detection: Identify long-running PHP requests and analyse their root causes with detailed slow log monitoring. Optimize scripts to reduce execution times and improve user experience.
• Customizable alerts: Set thresholds for critical PHP FPM metrics like memory usage, request processing time, or idle process count. Receive instant notifications when thresholds are reached.
• Integration with Web Servers: Seamlessly integrate Atatus PHP FPM Monitoring with popular web servers like NGINX, Apache, and Lighttpd. Track interactions between your web server and PHP FPM to ensure smooth communication.

Try your 14-day free trial of Atatus.

Sentry is a solid tool. But after a few months of growth, many teams hit the same wall: error volume spikes, the bill follows, and suddenly you're paying enterprise rates for a tool that still doesn't show you what's happening at the infrastructure level.

That's the most common reason teams start looking for alternatives — not because Sentry is broken, but because it's limited. It tracks errors and performance well, but it doesn't handle logs, infrastructure monitoring, or full-stack observability out of the box. For teams that need the full picture, that gap matters.

There's also the pricing model. Sentry charges by event volume, which sounds fine until an incident floods your quota. Other tools charge per host or per user, which scales more predictably as your team grows.

If you're evaluating your options, this guide covers 12 of the best Sentry alternatives in 2026 from lightweight error trackers to full observability platforms with honest pricing, pros and cons, and clear guidance on which tool fits which team.

• Top Sentry Alternatives
  1. Atatus
  2. Raygun
  3. Rollbar
  4. Instabug
  5. New Relic
  6. Bugsnag
  7. Datadog
  8. AppDynamics
  9. Splunk
  10. AppSignal
  11. Airbrake
  12. Dynatrace
• How to Choose the Right Sentry Alternative?

1. Atatus

Best for: Teams wanting a single platform for APM, RUM, logs, and infrastructure monitoring.

Atatus is a full-stack observability platform built for teams who want deep visibility without the complexity or cost of tools like Datadog or New Relic. It combines error monitoring, Application Performance Monitoring (APM), Real User Monitoring (RUM), infrastructure monitoring, and log management in a single unified dashboard, making it one of the most complete alternatives to Sentry available today.

While Sentry focuses primarily on error tracking and performance monitoring for specific services, Atatus gives you a broader picture. You can trace a user-impacting error from the browser all the way to the backend service and the infrastructure layer beneath it without switching tools.

Atatus collects rich contextual data when errors occur, including URL, user agent, viewport, app version, and more. Full stack traces are mapped directly to your source code, so developers spend less time reproducing bugs and more time fixing them. Atatus supports Java, PHP, Node.js, Ruby, Python, and Go.

Where Atatus Wins Over Sentry?

• Pricing clarity: Atatus uses straightforward per-host-hour billing vs. Sentry's event-volume model, which can spike unpredictably.
• Full-stack coverage: RUM + APM + Infrastructure in one platform; Sentry requires add-ons or separate tools for infra visibility.
• Log management: Atatus includes log monitoring natively; Sentry does not.

Atatus gathers a range of additional information when an error arises, including URL, port, user, app, version, user agent, viewport, and more. This data enriches your understanding of the issue and aids in efficient debugging.

With Browser Monitoring, Application Performance Monitoring (APM), and Infrastructure Monitoring, it delivers real-time insights into frontend, backend, and server performance, enhancing observability.

Atatus Application Performance Monitoring (APM) offers in-depth insights into your applications, allowing you to pinpoint performance bottlenecks, resolve issues, and enhance your services. Atatus APM ensures optimal performance and user experience for your applications.

Atatus supports a variety of programming languages, including Java, PHP, Node.js, Ruby, Python, and Go, ensuring it is compatible with a diverse range of applications.

Pricing

2. Raygun

Best for: Teams needing detailed crash reporting with strong mobile and desktop coverage.

Raygun is a developer-focused error monitoring and APM platform that excels at crash reporting across web, desktop, and mobile platforms. It provides three core capabilities: Crash Reporting for real-time error detection, Real User Monitoring (RUM) for front-end performance insights, and Application Performance Monitoring (APM) for back-end code execution tracing.

Where Raygun stands out from Sentry is in the richness of crash detail it provides. Each error report includes full stack traces, environment variables, browser details, version information, class names, host data, and affected user counts — making it easier to prioritize bugs that actually affect users rather than ones that are simply frequent.

Raygun also offers a unique 'affected users' metric that lets teams see impact at a glance before diving into stack traces. This makes triage faster compared to Sentry's default error grouping view.

Where Raygun Wins Over Sentry?

• User impact metrics: Raygun surfaces how many real users are affected by each error upfront, making triage easier.
• Deployment tracking: Release version pinpointing is more granular than Sentry's out-of-the-box setup.
• Cross-platform: Strong native support for both web and mobile (iOS/Android) in one tool.

Raygun offers three key features: Error Monitoring & Crash Reporting for quick issue resolution, Real User Monitoring for enhanced front-end performance insights, and Application Performance Monitoring for streamlined code execution observation.

With Raygun, gain comprehensive visibility across your entire tech stack with real-time error monitoring for desktop and mobile platforms. Never overlook errors again as you receive detailed insights including full stack traces, environment variables, browser details, version information, class names, hosts, and more.

Pros & Cons

Pricing

3. Rollbar

Best for: Teams with high deployment frequency that need real-time error detection per release.

Rollbar is a purpose-built error monitoring platform with a focus on fast deployment cycles. It links errors directly to code commits and releases, allowing developers to see exactly which deployment introduced a regression, a capability that goes beyond Sentry's default error grouping.

Rollbar automatically groups similar errors to reduce alert noise, and lets teams create custom grouping rules or use AI-suggested groupings. It's particularly effective for teams deploying multiple times per day, with reported improvements in deployment frequency of up to 9x for companies using it.

The platform collects errors from nearly any major platform or framework and provides real-time visibility into exception messages and stack traces, traceable to the specific line of code, source code commit, or release.

Where Rollbar Wins Over Sentry?

• Deploy tracking: First-class support for linking errors to specific code commits and deploy IDs better integration than Sentry for CI/CD workflows.
• Smarter grouping: AI-assisted error grouping reduces duplicate alerts more effectively than Sentry's default rules.
• Generous free tier: The Basic plan is permanently free with a reasonable error volume.

Pros & Cons

Pricing

4. Instabug

Best for: Mobile-first teams building iOS, Android, React Native, or Flutter apps.

Instabug is a mobile-specific monitoring platform that fills a gap Sentry doesn't fully address: in-app bug reporting and crash tracking designed specifically for mobile development workflows. It offers Bug Reporting with user feedback, Crash Reporting, and App Performance Monitoring (APM) in a mobile-native interface.

One of Instabug's unique features is its 'App Apdex number' — a single health metric that summarizes your app's overall stability and experience, making it easy for teams to prioritize work. It also provides real-time release management, helping mobile teams balance stability, performance, and feature development simultaneously.

Instabug allows developers to see user sessions during which errors occurred, with full device details, network conditions, and logs attached — giving far more mobile context than Sentry's mobile SDKs.

Where Instabug Wins Over Sentry

• Mobile-first UX: Built ground-up for mobile, not adapted from web tooling.
• In-app feedback: Users can shake their device to submit bug reports with screenshots and device state attached.
• Session context: Full session replay and device state capture during crashes.

Pros & Cons

Pricing

5. New Relic

Best for: Larger engineering teams needing full-stack observability with a broad ecosystem.

New Relic is one of the most comprehensive observability platforms available, covering APM, infrastructure monitoring, log management, synthetic monitoring, and more in a single interface. For teams outgrowing Sentry's error-centric view, New Relic provides the full observability stack that enterprise engineering teams need.

New Relic's Errors Inbox feature tightly integrates with APM data to surface application-specific errors alongside performance context like timestamps, affected users, hosts, and URLs without additional configuration. Its AI-powered alerting reduces noise by surfacing anomalies rather than just threshold breaches.

However, New Relic's pricing model is complex. It charges based on data ingest (GB) and user seats, which can scale unpredictably as teams grow. A 50-person engineering team with full platform access can expect to pay $4,950/month in user licenses alone before accounting for data costs.

Where New Relic Wins Over Sentry

• Depth of observability: Covers logs, traces, metrics, and synthetics natively. Sentry only covers errors and performance.
• 500+ integrations: Broader ecosystem than Sentry, covering cloud providers, databases, and infrastructure.
• Generous free tier: 100GB data + 1 full platform user free. One of the best free tiers in the industry.

Pros & Cons

Pricing

6. Bugsnag

Best for: JavaScript-heavy teams using React, Vue, or Angular who want stability scoring.

Bugsnag focuses on error monitoring and real user monitoring with a particular strength in JavaScript frameworks. It supports React, Vue, Angular, and vanilla JavaScript natively, making it an excellent Sentry alternative for frontend-heavy teams.

What differentiates Bugsnag from Sentry is its stability score feature — a percentage-based metric that tracks what proportion of user sessions are error-free. This gives teams a single health number to track against a stability target, rather than raw error counts. The timeline investigation feature also enables teams to correlate error spikes with deploys, feature flags, or external events.

Bugsnag's end-to-end diagnostics provide real-time analysis, allowing for a deep understanding of problems as they occur, with custom error views that help teams prioritize the most critical issues.

Where Bugsnag Wins Over Sentry?

• Stability score: A unique metric Sentry doesn't offer, tracks the % of user sessions that are error-free.
• JavaScript-first: Deeper framework support for React, Vue, Angular than Sentry's generic JS SDK.
• Affordable entry tier: Free plan with essential features, $18/month for growing teams.

Pros & Cons

Pricing

7. Datadog

Best for: Cloud-native teams needing unified infrastructure, APM, and error monitoring.

Datadog is one of the most powerful cloud monitoring platforms available, offering a unified solution that covers infrastructure, APM, logs, error tracking, security, and real user monitoring. For teams where application errors are deeply linked to infrastructure issues, Datadog's ability to correlate errors with host metrics, container logs, and network conditions in a single view is unmatched.

Datadog's error tracking groups errors across both frontend and backend applications, with proactive alerts powered by its Watchdog AI/ML engine that detects anomalies before they escalate. Its APM solution tracks performance across browsers, databases, and specific code paths with end-to-end distributed tracing.

The tradeoff with Datadog is cost. Its pricing is complex, with separate billing for APM, logs, infrastructure, and additional products. Teams often find the bill higher than expected as they enable more features. It is best suited for mid-size to large engineering organizations.

Where Datadog Wins Over Sentry?

• Infrastructure correlation: Ties errors directly to host and container health. Sentry has no infrastructure layer.
• Watchdog AI: Proactive anomaly detection without manual alert threshold setup.
• Single-pane-of-glass: All telemetry such as logs, traces, metrics, errors visible in one place.

Pros & Cons

Pricing

Datadog uses modular pricing; each product (APM, Logs, Infrastructure, RUM, etc.) is billed separately. APM starts at approximately $31/host/month. Contact Datadog for a full quote based on your stack.

8. AppDynamics

Best for: Large enterprises using Cisco infrastructure with complex business transaction monitoring needs.

AppDynamics, now part of Cisco, is an enterprise-grade APM solution that goes beyond error tracking to monitor business metrics, user experience, infrastructure, and security in a unified platform. Its defining strength is Business Transactions — a feature that lets teams model and monitor the specific workflows that matter to their business (e.g., checkout flows, payment processing, order fulfillment) rather than just tracking HTTP endpoints.

AppDynamics uses an agent-based licensing model that charges per CPU core, per page view (for browser monitoring), and per application server which gives it strong coverage in traditional enterprise environments. However, in high-density container environments, costs can spike unexpectedly, as each instance may require a separate license unit.

The platform offers swift root-cause analysis with AI-assisted anomaly detection, and integrates tightly with the broader Cisco security and networking stack. It is best suited for large organizations already embedded in the Cisco ecosystem.

Where AppDynamics Wins Over Sentry?

• Business transaction monitoring: Monitor complete business workflows, not just raw API performance.
• Enterprise Cisco integration: Deep integration with Cisco networking and security stack.
• Proactive root cause analysis: AI-powered instant root cause identification with real-time pinpointing.

Pros & Cons

Pricing

AppDynamics pricing is available upon request. Based on third-party data, a comparable enterprise deployment typically costs $50,000–$90,000 annually. Full-stack monitoring for large organizations can exceed $150,000–$300,000+ per year.

9. Splunk

Best for: Organizations with complex log analysis needs in microservices or multi-cloud environments.

Splunk APM is a cloud-native monitoring solution purpose-built for microservices architectures. Unlike Sentry which primarily focuses on application errors, Splunk is a data platform that aggregates logs, metrics, and traces from across your entire infrastructure, then applies powerful search and analytics to identify error patterns, trends, and root causes.

Splunk's distributed tracing captures the full path of a request across microservices, identifying latency and errors at each step. Its alerting and dashboard capabilities are highly customizable, and its log analytics are best-in-class for organizations managing high volumes of operational data.

Splunk is not a lightweight tool. It is built for organizations with significant log and telemetry volumes and dedicated operations teams. It is significantly more complex to configure than Sentry, but the analytical depth it offers is unmatched for log-heavy environments.

Where Splunk Wins Over Sentry

• Log analytics depth: Best-in-class search across logs, metrics, and traces. Sentry is not a log management tool.
• Microservices tracing: Full end-to-end distributed tracing across complex service meshes.
• Scalability: Built to handle petabyte-scale telemetry volumes.

Pros & Cons

Pricing

Splunk pricing is usage-based and available upon request. Costs scale with data ingest volume and are generally best suited for mid-to-large organizations. Contact Splunk for a quote.

10. AppSignal

Best for: Ruby, Elixir, and Node.js teams wanting simple, predictable monitoring with no surprise bills.

AppSignal is a developer-friendly monitoring platform that combines error tracking, performance monitoring, uptime monitoring, log management, and host metrics in one tool. It is particularly well-regarded in the Ruby, Elixir, and Node.js communities, where it offers deep framework-level integrations with Rails, Phoenix, and Express.

What makes AppSignal stand out from Sentry and most competitors is its pricing philosophy. AppSignal has no overage fees and no hard limits. If you exceed your plan for a month, monitoring continues uninterrupted and AppSignal only contacts you about an upgrade if you go over your plan for 2 out of 3 consecutive months. For budget-conscious teams, this is a meaningful difference from Sentry's event-volume pricing, which can spike without warning.

AppSignal also offers transparent request-based pricing with a free tier (introduced in October 2025), making it accessible to solo developers and small teams. All features are available on every plan, no feature walls.

Where AppSignal Wins Over Sentry

• No surprise bills: Monitoring continues even past plan limits; no punitive overage charges.
• All features on every plan: Unlike Sentry's tiered feature access, AppSignal doesn't lock features behind higher tiers.
• Ruby/Elixir depth: Far better native integration for Rails and Phoenix apps than Sentry.

Pros & Cons

Pricing

11. Airbrake

Best for: Budget-conscious teams needing solid error monitoring across a diverse tech stack.

Airbrake is a lightweight error monitoring and APM tool with one of the widest language compatibility lists available, supporting over 50 programming languages, frameworks, and libraries. If your team uses a less common stack, Airbrake is one of the few tools that likely supports it out of the box.

Airbrake's advanced alerting and notification system allows teams to customize alerts by error severity, frequency, or error type, ensuring developers receive notifications for critical issues without being overwhelmed by noise. Its APM tracks HTTP requests, response times, error rates, and user satisfaction scores across all monitored services.

Compared to Sentry, Airbrake is simpler and more lightweight, making it well-suited for smaller teams or individual developers who need core error monitoring without the complexity of a full observability platform.

Where Airbrake Wins Over Sentry

• Breadth of language support: 50+ languages; useful for polyglot teams or unusual tech stacks.
• Pricing transparency: Clear monthly pricing with no per-event complexity.
• Enriched contextual data: Parameters, users, and environment variables attached to every error report.

Pros & Cons

Pricing

12. Dynatrace

Best for: Enterprise teams wanting AI-automated root-cause analysis with minimal manual configuration.

Dynatrace is an enterprise-grade observability platform that differentiates itself with Davis, its AI-powered causal analysis engine. While Sentry requires engineers to manually investigate error context and correlate data across services, Dynatrace's Davis AI automatically identifies root causes by correlating logs, metrics, traces, and user session data, often pinpointing the underlying issue before an alert even fires.

Dynatrace provides end-to-end distributed tracing across microservices, automatic discovery of infrastructure dependencies, and cloud-native observability for AWS, Azure, and GCP environments. Its OneAgent auto-instruments your environment, meaning less manual setup than Sentry or most Sentry alternatives.

The platform is designed for organizations with complex hybrid environments, those running microservices, Kubernetes workloads, and multi-cloud deployments simultaneously. It includes unlimited users at no extra cost, which is a notable advantage over New Relic's per-user pricing model.

Where Dynatrace Wins Over Sentry

• Davis AI: Auto-detects root causes without manual investigation. Sentry gives you data; Dynatrace gives you answers.
• Auto-instrumentation: OneAgent auto-discovers and instruments your stack without manual SDK setup per service.
• Unlimited users: No per-seat cost for additional team members, a significant advantage for larger organizations.

Pros & Cons

Pricing

How to Choose the Right Sentry Alternative?

To choose the right tool from various Sentry alternatives, it is important to consider several factors. Each tool offer unique strengths and weaknesses, so it's essential to prioritize features based on your specific needs.

1. More Than Just Error Tracking: If you prioritize error tracking and basic application monitoring, tool like Raygun may be adequate. But if you want full visibility into your application performance, choose comprehensive performance monitoring tools tools like Datadog, Atatus and New Relic.
2. User Interface and Ease of Use: Choose a tool that is easy to use and quick to set up. At Atatus, we keep things simple. Our tool is easy to use with a friendly interface and quick setup.
3. Features: Evaluate the features offered by the alternative tool and compare them with Sentry. Consider aspects such as error tracking, real-time monitoring, alerting mechanisms, integrations with other tools and platforms.
4. Pricing: Be mindful of the pricing details for each tool before making a choice. Some, like Datadog are based on complex billing structure,  while others like Atatus may offer more straightforward pricing schemes.
5. Alerting: Ensure that the tool offers effective alerting solutions that notify developers of critical issues. Effective alerting ensures timely responses to problems, minimizing downtime.

Get Started with Atatus

With just a few clicks, you can begin monitoring errors, application performance, server metrics, and logs in one unified platform. Intuitive interface and minimal configuration make it easy for teams to gain deep visibility and resolve issues quickly.

FAQs

1. What are the top alternatives to Sentry for error tracking?

Some of the best Sentry alternatives for error tracking include Atatus, Rollbar, Bugsnag, Raygun, and LogRocket. These Sentry competitors offer robust features such as real-time alerts, application performance monitoring, and user-friendly interfaces, making them ideal for teams seeking simpler or more affordable Sentry tools.

2. How does Atatus compare to Sentry in terms of features?

In the Sentry vs Atatus comparison, both tools offer powerful error tracking and application performance monitoring. However, Atatus stands out with its all-in-one platform, offering log management, end-to-end tracing, and easier setup, making it a strong Sentry alternative for teams that prefer simplicity without compromising on insights.

3. Do Sentry alternatives support multiple programming languages?

Yes, most Sentry alternatives support a wide range of programming languages including JavaScript, Python, Ruby, Java, PHP, and more. These Sentry competitors are designed to fit into diverse tech stacks, just like Sentry APM, ensuring broad compatibility and seamless integration.

4. What are the key features to consider when choosing a Sentry alternative?

When evaluating Sentry alternatives, consider features like real-time error tracking, application performance monitoring, end-to-end distributed tracing, language support, ease of use, and pricing. The best Sentry tools combine error insights and performance data in a single, intuitive dashboard.

5. Do any Sentry alternatives offer end-to-end distributed tracing?

Several alternatives to Sentry, including Atatus, support end-to-end distributed tracing. This feature allows developers to trace transactions across services, identify bottlenecks, and optimize performance, making these Sentry competitors effective for full-stack application performance monitoring.

6. Are there Sentry alternatives with built-in dashboards and reporting?

Yes, most Sentry tools like Atatus, Bugsnag, and Raygun include built-in dashboards and customizable reports. These features provide teams with clear visibility into error trends, app health, and performance metrics, essential for tracking the same way you would with Sentry APM.

7. Can I replace Sentry with a tool that includes both error monitoring and performance metrics in one dashboard?

Absolutely. Tools like Atatus offer unified dashboards that combine error tracking and application performance monitoring, making them strong Sentry alternatives. This all-in-one visibility enables teams to monitor and resolve issues faster compared to managing separate tools.

Your on-call engineer gets paged. A critical service is down. Error rates are spiking.

They open Sentry for errors. Flip to Grafana for metrics. Pivot to Kibana to search logs. Then jump to Lumigo, but that only covers the Lambda functions, not the Node.js backend throwing the actual errors. Three tabs become five. Five become eight. Half the incident is gone and your team is still piecing together what happened instead of fixing it.

Sound familiar?

This is the daily reality for most DevOps and SRE teams today. Not because they chose the wrong tools individually. Sentry is excellent at error tracking, Grafana is a powerful visualization layer, ELK is battle-tested for log search, but because stitching four specialized tools together creates an observability stack that is fundamentally broken at the seams.

Increasingly, engineering teams are figuring this out and making a move. They're replacing their fragmented monitoring stacks with a single unified platform. And Atatus is the tool they're switching to.

This post breaks down exactly which tools teams are replacing, why those tools fall short despite their individual strengths, and what tangible benefits DevOps and SRE teams experience within weeks of making the switch to Atatus.

What's in this article?

• Why Switching Tools Won't Fix Your Observability Problem?
• The 3–4 Tools DevOps and SRE Teams Are Replacing
• What DevOps and SRE Teams Actually Gain?
• Before and After: A Real Incident Response Comparison
• What About Migration? Is It Worth the Disruption?

Why Switching Tools Won't Fix Your Observability Problem?

Here is a confession most engineering leaders won't say out loud: the problem is rarely any single monitoring tool. Each tool you're using probably does its job reasonably well in isolation. The problem is that observability is a cross-cutting concern, and you're solving it with point solutions that weren't designed to talk to each other.

When an incident happens in a modern distributed system, the signal that something is wrong rarely tells you where it went wrong. You need to correlate:

• A spike in error rate (Sentry/error tracker)
• Elevated p95 latency on a specific endpoint (APM tool)
• A burst of slow database queries (DB monitoring or APM)
• Log lines from three microservices (log aggregation tool)
• A spike in Lambda cold starts that started at the exact same time (Lumigo or serverless tool)
• Infrastructure CPU and memory on the host that ran the affected service (infrastructure monitor)

When those six data streams live in six different products with six different UIs, six different login sessions, and zero native correlation between them, you're not doing observability. You're doing manual detective work under pressure, in the middle of the night, while the business is losing money.

Not by being a marginally better version of any one tool, but by being the only platform where logs, metrics, traces, real user data, synthetic checks, infrastructure health, serverless functions, and database performance live on the same timeline, in the same UI, connected natively.

The 3-4 Tools DevOps and SRE Teams Are Replacing

Let's get specific. Here are the most common tool combinations teams are ripping out and replacing  and exactly what they're gaining by doing so.

Tool #1 Being Replaced: Sentry (Error Tracking)

What Sentry does well: Sentry is arguably the best standalone error tracking tool in existence. It captures exceptions beautifully, groups them intelligently, and surfaces stack traces with useful context.

Where Sentry falls short: Sentry only sees errors. When you get an alert that a NullPointerException is spiking in your checkout service, Sentry can tell you the error happened, but it can't tell you why. Was it a slow database query that timed out upstream? Was it a memory pressure issue on the host? Was a recent deployment the trigger? Was it the Lambda cold start in the payment function cascading into the backend?

To answer any of those questions, you leave Sentry and open three other tools. You've broken your focus and lost the thread of what you were looking at.

What Atatus gives you instead: Atatus captures and groups errors with the same quality as Sentry, but every error is natively linked to the APM trace that caused it, the infrastructure metrics of the host at the time, and the log lines from surrounding services. You click an error and you see the full story, not just the symptom.

You also get session replay baked in, so for frontend errors, you can watch exactly what the user was doing when the error occurred. Sentry requires a separate integration for session replay. In Atatus, it's part of the same platform.

Also read: Learn how different observability platforms compare in our Sentry alternatives guide.

Tool #2 Being Replaced: Grafana + Prometheus (Metrics & Dashboards)

What Grafana + Prometheus does well: This is the gold standard for metrics visualization and collection in open-source infrastructure monitoring. Prometheus scrapes metrics with incredible flexibility. Grafana renders them into beautiful dashboards.

Where it falls short: Grafana and Prometheus are infrastructure for building observability, they're not observability out of the box. To get value from them, your team needs to:

• Set up and maintain Prometheus exporters for every service
• Write and maintain PromQL queries (a non-trivial skill)
• Build and maintain dashboards from scratch
• Manage alert rules in a separate alerting system (Alertmanager)
• Maintain the infrastructure to run all of this at scale

And critically: Grafana shows you metrics. It doesn't explain them. When a dashboard goes red during an incident, you still have to manually correlate that metric spike with your logs, traces, and error data in other tools.

What Atatus gives you instead: Atatus collects infrastructure metrics automatically with lightweight agents with no PromQL expertise required, no exporter maintenance, no dashboard-from-scratch engineering. Pre-built dashboards are ready from day one. Custom dashboards can be built in minutes with a point-and-click interface.

More importantly, when a metric spikes in Atatus, you're one click away from the correlated APM traces, error events, and log entries from the same time window. The dashboard is a starting point for diagnosis, not an end point.

The real cost many teams miss: Running Prometheus and Grafana at production scale isn't free. You're paying engineering time to maintain it, storage costs for metric retention, and compute costs to run the infrastructure. Teams that switch to Atatus routinely find their total cost of ownership drops even though they're now paying for a SaaS product.

Also read: Explore other monitoring tools in our guide to Grafana alternatives.

Tool #3 Being Replaced: ELK Stack / Splunk (Log Management)

What ELK and Splunk do well: Elasticsearch, Logstash, and Kibana form one of the most powerful log search and analytics platforms available. Splunk is the enterprise standard for log aggregation with powerful query capabilities. If you need to search through billions of log events, both tools can do it.

Where they fall short:

ELK: The secret about self-hosted ELK is that it's a full-time job. Elasticsearch clusters need constant tuning, capacity planning, and maintenance. Index lifecycle management, shard configuration, and cluster health become specialized knowledge that someone on your team has to own. For many engineering teams, ELK has become a tool that also needs to be monitored and maintained, adding to operational burden instead of reducing it.

Splunk: Splunk's capabilities are extraordinary and its pricing is extraordinary in the same direction. Splunk charges based on data ingestion volume, and in a modern microservices environment where logs are generated continuously at high volume, those costs can spiral to six figures annually with terrifying speed. Teams have been hit with bills they didn't anticipate simply because traffic scaled up.

What Atatus gives you instead: Atatus provides centralized log management with real-time search, filtering, and analysis without the operational overhead of running your own Elasticsearch cluster, and without Splunk's volume-based pricing model that punishes you for having reliable, well-instrumented systems.

Critically, Atatus correlates log events with APM traces and errors in the same view. When you're debugging a slow request, you see the trace span timeline alongside the exact log lines emitted during that request's execution.

Tool #4 Being Replaced: Lumigo (Serverless Monitoring)

What Lumigo does well: Lumigo is genuinely excellent at what it does: monitoring AWS Lambda functions. Auto-instrumentation with no code changes, payload capture for debugging, cold start tracking, and visual distributed tracing across async flows like SQS, SNS, and Step Functions. If you only run serverless on AWS, Lumigo is a polished, purpose-built tool.

Where Lumigo falls short: The keyword is only. Lumigo is a serverless-first, AWS-centric tool. The moment your architecture extends beyond Lambda, which virtually every production system does, Lumigo’s visibility ends.

Your Node.js API servers? Not covered. Your PostgreSQL database queries? Not covered. Your React frontend performance and Core Web Vitals? Not covered. Your Kubernetes pods and container metrics? Not covered. Your infrastructure CPU and memory? Not covered. Azure Functions or Google Cloud Functions? Limited or no support.

So teams using Lumigo end up needing at least two other tools to cover the rest of their stack, recreating exactly the fragmentation problem they were trying to solve.

What Atatus gives you instead: Atatus monitors serverless functions, including AWS Lambda, Azure Functions, and Google Cloud Functions, along with your traditional backend services, infrastructure, databases, and frontend user experience in one unified platform.

When a user reports a slow checkout experience, you can trace the journey: the frontend page load (Real User Monitoring), through the API call (APM), into the Lambda function that handled the payment logic (Serverless Monitoring), to the database query that was the actual bottleneck (Database Monitoring). That complete story, in one product, on one screen.

Lumigo can only show you one chapter. Atatus shows you the whole book.

Also read: See how modern monitoring platforms compare in our Lumigo alternatives guide.

Bonus: What Teams Also Stop Needing

Beyond the primary four, teams switching to Atatus typically find they can also eliminate:

• Pingdom / UptimeRobot → Replaced by Atatus Synthetic Monitoring, which simulates real user interactions globally and alerts before users are affected
• New Relic Browser / FullStory → Replaced by Atatus Real User Monitoring and Session Replay
• Datadog APM → Replaced by Atatus APM with distributed tracing, at a fraction of the cost
• Standalone Kubernetes monitoring tools → Replaced by Atatus Kubernetes Monitoring with pod and container health visibility

What DevOps and SRE Teams Actually Gain?

Replacing tools isn't worth the migration pain unless the benefits are substantial and concrete. Here's what teams consistently report after switching to Atatus.

Benefit #1: Incident Response Time Drops by 40–60%

This is the number that gets engineering leaders' attention fastest.

When your entire observability stack is in one place, the gap between "alert fires" and "engineer understands what's happening" shrinks significantly. No tab-switching, no hunting across tools, no "can you send me the relevant Splunk query" messages in Slack.

In Atatus, an alert fires with full context attached: the metric that triggered it, the correlated traces, the relevant log lines, and the affected infrastructure. The engineer on call opens one screen and sees the incident's full picture immediately.

Teams migrating from fragmented stacks consistently report cutting their Mean Time to Resolve (MTTR) in half. For an SRE team managing a service with an SLO of 99.9% uptime, that improvement can be the difference between meeting and missing your reliability targets.

Benefit #2: Engineering Time Shifts from Maintenance to Product

Here's a cost that never shows up on a vendor invoice: the engineering hours spent maintaining your monitoring stack.

Running self-hosted ELK means someone is tuning Elasticsearch clusters, managing index lifecycle policies, and debugging why the Kibana dashboard is slow. Running Prometheus means someone is maintaining exporters, writing PromQL, and capacity planning storage.

When teams switch to Atatus, all of that maintenance work disappears. There are no agents to tune, no clusters to run, no storage to capacity-plan. Atatus agents deploy with minimal configuration and need essentially no ongoing attention.

The engineering time freed up by this is real. A senior engineer who was spending part of their week on monitoring infrastructure can redirect that capacity to product work instead.

Benefit #3: Licensing Costs Consolidate Significantly

Let's run the numbers that most teams are embarrassed to add up.

A mid-size engineering team with 20 developers running a modern microservices architecture might be paying:

• Sentry: $26/month per developer = ~$520/month
• Grafana Cloud (for managed Prometheus): $200–$800/month depending on usage
• Splunk or Elastic Cloud: $500–$2,000/month depending on log volume
• Lumigo: $300–$800/month depending on invocations
• Pingdom or similar: $100–$300/month

That’s $1,620 to $4,420 per month for a stack that still doesn’t give you correlated observability. Add in the hidden costs, such as engineering time for maintenance, integration overhead, and storage for duplicate data, and the true total cost of a fragmented stack is often 2 to 3 times the invoice total.

Atatus's transparent, host-based pricing model means you pay for what you actually use without hidden per-seat fees, per-trace charges, or data ingestion penalties that punish you for running a well-instrumented system. Teams consistently find that consolidating to Atatus reduces their total observability spend by 40–60% while actually increasing their coverage and capability.

Benefit #4: Onboarding New Team Members Goes from Weeks to Days

This is the benefit that compounds over time as teams grow.

When a new engineer joins a team using four different monitoring tools, they need to learn four different UIs, four different query languages, four different alert paradigms, and the undocumented tribal knowledge of how those tools are connected. For a senior hire, this is a few weeks of friction. For a mid-level hire, it's a month.

When a new engineer joins a team using Atatus, they learn one platform. One UI, one query approach, one mental model. The connection between logs, metrics, traces, and user data is built in, so they do not need to learn an undocumented convention for how your team links Grafana dashboards to ELK searches.

This scales as teams grow. The knowledge senior engineers build about using Atatus for incident response is transferable. You stop needing a Splunk expert and a separate Grafana expert in the same room during every incident.

Benefit #5: Full Coverage Across Every Layer of Your Stack

The most frustrating problem with fragmented monitoring is what falls through the gaps, the parts of your stack that no single tool covers because each tool was built for one layer.

A serverless tool doesn't see your VMs. An infrastructure monitor doesn't see your Lambda cold starts. An error tracker doesn't see your database query plans. An APM tool doesn't capture what real users are experiencing in their browsers.

Atatus covers all of it from one account. The platform includes:

• APM: full distributed tracing across services, with transaction-level detail, slow query detection, and dependency mapping
• Infrastructure Monitoring: servers, VMs, containers, Kubernetes pods, with CPU, memory, disk, and network metrics
• Log Management: structured and unstructured logs from all services, correlated to traces and errors
• Real User Monitoring (RUM): actual user experience data from browsers and devices, including Core Web Vitals
• Synthetic Monitoring: proactive uptime and performance checks from global locations, before users notice
• Serverless Monitoring: AWS Lambda, Azure Functions, and Google Cloud Functions with cold start tracking and distributed tracing
• Database Monitoring: query performance, slow queries, connection pools, and bottleneck detection
• Session Replay: visual playback of user sessions to understand exactly what happened during errors
• API Analytics: visibility into how APIs are used, which endpoints are slow, and where failures occur
• Kubernetes Monitoring: pod health, resource utilization, and cluster-level observability

This isn't a list of future roadmap items. These are production-ready capabilities in a single Atatus account, with a single agent deployment, managed from a single dashboard.

Benefit #6: Smarter Alerts That Actually Mean Something

Static alert thresholds are a maintenance burden. Set them too low and you drown in noise. Set them too high and you miss real issues until users are already affected.

Atatus uses anomaly detection across your metrics, traces, and logs to flag deviations from what's normal for each specific service. A 200ms latency increase during a known batch job looks different from the same spike with no explanation. The platform understands that difference.

During incident response, Atatus connects signals across your stack automatically. Instead of manually cross-referencing five dashboards to piece together a timeline, you see what changed, when it changed, and what else moved at the same time in one view.

Before and After: A Real Incident Response Comparison

Before Atatus (With 4 Tools)

PagerDuty fires. Error rate on checkout is at 15%.

Sentry shows PaymentProcessingException spiking but not why. Grafana shows CPU and memory are fine. Kibana eventually surfaces timeout errors pointing at the payment Lambda. Lumigo confirms elevated cold starts but can't explain them and has no connection back to Grafana. They end up in CloudWatch, manually piecing together that a recent deployment triggered the cold starts.

Rollback initiated. Incident resolved. Most of that time was spent finding the answer, not fixing it.

After Atatus (One Unified Platform)

PagerDuty fires. The Atatus alert already says: error rate elevated, correlated with a Lambda cold start spike and a recent deployment.

The engineer opens one screen, sees the full sequence on one timeline, clicks into the APM trace, confirms the cause, and initiates the rollback.

Time spent searching: almost none.

What About Migration? Is It Worth the Disruption?

One of the main reasons teams stick with fragmented stacks even when they're painful is fear of migration. Ripping out four tools and replacing them sounds like months of work and weeks of risk.

With Atatus, the reality is different.

• Setup is straightforward. The Atatus agent auto-instruments your applications and infrastructure with minimal configuration. Most teams have real data in the platform on day one.
• No need to rip everything out at once. Run Atatus alongside your existing tools, validate coverage, then retire them one at a time.
• Nothing new to learn conceptually. Traces, logs, metrics, dashboards, and alerts are the same concepts you already work with, just in one place instead of four.
• OpenTelemetry native. Already instrumenting with OTel? Connecting to Atatus takes minimal extra work, and your data stays portable regardless.

Who Is Moving to Atatus?

• Growing startups paying full price for four tools that still don't talk to each other. Atatus typically costs less than the combined total, with broader coverage.
• Scale-ups on hybrid architectures running serverless, containers, and VMs together is exactly where tools like Lumigo hit their ceiling.
• Enterprise SRE teams under pressure to improve reliability who've identified tool-switching as the biggest drag on incident response.
• DevOps teams with lean headcount that can't realistically maintain a dedicated ELK setup, a Grafana expert, and a Splunk admin at the same time.

Conclusion

The monitoring tool problem is not a tool problem. It's a structure problem.

Every tool in your current stack does its specific job well. But when an incident happens, you do not need four specialists. You need one complete picture. And that cannot be stitched together from tools that do not share a data model or a timeline.

Fewer tools. Less maintenance. Lower cost. Faster incidents. That's what teams find on the other side of the switch.

Frequently Asked Questions

Does Atatus fully replace Sentry, or do some teams run both?

Most teams replace Sentry entirely. Every error in Atatus is already linked to the trace and logs that caused it. Instead of manually connecting dots across two tools, you just click the error and see why it happened. Session replay is included in the same account too.

We've built a lot of Grafana dashboards over the years. Do we lose that work?

Your existing Grafana dashboards can be rebuilt in Atatus using pre-built templates or a simple drag-and-drop builder. Most teams find it faster than expected and the dashboards actually become more useful because every metric links directly to the traces and logs from the same window.

We use Lumigo specifically for Lambda payload inspection. Does Atatus support that?

Atatus covers cold start tracking, invocation errors, and distributed tracing across async flows like SQS and SNS. For deep payload-level inspection, it's worth a conversation with the Atatus team about your specific use case. For most day-to-day production monitoring needs, Atatus's serverless coverage handles what teams actually need with the advantage of connecting Lambda data to the rest of your backend and infrastructure in one place.

The backbone of modern software development is automation and at the heart of that lies the CI/CD pipeline. It’s what turns code into deployable software, delivering changes to users faster, safer, and more predictably.

In simple terms, a CI/CD pipeline automates everything from the moment developers push code to when it reaches production. It integrates, tests, builds, and deploys software continuously ensuring faster releases with fewer human errors.

But setting up a CI/CD pipeline isn’t enough. Without observability and monitoring, even the most automated systems can fail silently, bringing releases to a halt or letting critical bugs slip into production.

This post breaks down everything you need to know from what CI/CD means and why monitoring matters, to how observability strengthens reliability and security.

What's in this blog?

1. What Is Continuous Integration (CI)?
2. What Is Continuous Deployment (CD)?
3. Why Should You Monitor the CI/CD Pipeline?
4. Things to Consider When Developing CI/CD Pipelines
5. Observability in CI/CD Pipelines
6. Strengthening Security in the CI/CD Pipeline
7. Scanning Methods for Securing Pipelines
8. Keep Pipelines Secure with Continuous Monitoring and Auditing
9. Conclusion

What Is Continuous Integration (CI)?

Continuous Integration (CI) is the process of frequently merging code changes into a shared repository where automated builds and tests run every time. The idea is simple: catch issues early, fix them fast, and ensure that every new code commit works with the existing codebase.

When CI is done right, integration issues are spotted before they snowball into major production incidents.

The Core Stages of Continuous Integration

• Code Commit
  Every developer commits code to a version control system like Git. Each commit is tracked and reversible, ensuring transparency and accountability across the team.
• Static Code Analysis
  Once code is pushed, automated scans powered by SAST (Static Application Security Testing) check for vulnerabilities, syntax errors, and poor coding practices before the build starts.
• Build
  The build stage compiles your application, runs unit tests, and prepares the code for deployment. Build verification, artifact storage, and dependency management also happen here.
• Test
  Automated tests validate that new code behaves as expected. These include sanity, integration, regression, and stress tests designed to prevent bugs from reaching production. A well-structured CI pipeline can run hundreds of tests in minutes, giving teams the confidence to ship continuously.

What Is Continuous Deployment (CD)?

Continuous Deployment (CD) is the second half of the CI/CD equation. It automates the release process so that every successfully tested build is automatically deployed to production or staging environments. The goal is simple: deliver updates fast, consistently, and without manual intervention.

Continuous Deployment Process

• Bake and Deploy
  Before code goes live, it’s deployed to staging or beta environments where QA teams and product owners validate functionality. Once the build proves stable, it’s promoted to production automatically.
• Monitoring
  After deployment, real-time monitoring takes over. Application Performance Monitoring (APM) tools track latency, error rates, throughput, and system health. If a new release triggers a spike in response time or CPU usage, the system flags it immediately.
• Feedback and DevOps Collaboration
  Feedback loops complete the process. Developers, QA, and operations teams receive automated updates about each release through Slack, email, or ITSM tools. This continuous feedback fosters rapid iteration and reinforces a culture of continuous improvement.

Why Should You Monitor the CI/CD Pipeline?

A CI/CD pipeline is a living system. It touches every part of your software delivery lifecycle including code commits, builds, tests, deployments, and monitoring. When one component fails, the entire process slows or stops.

Monitoring ensures you can detect issues before they impact production. It gives you complete visibility into each stage to pinpoint where delays, failures, or misconfigurations occur.

Benefits of Monitoring Your Pipeline

• Early Detection of Failures: Detect issues early by continuously monitoring pipelines and identifying patterns such as regressions and flaky test detection, enabling teams to fix unstable tests before they impact production.
• Increased Reliability: Keep your release cadence consistent by ensuring every stage performs as expected.
• Improved Developer Productivity: Reduce time spent troubleshooting broken pipelines.
• Faster Time-to-Market: Resolve issues quickly and maintain a predictable delivery rhythm.

Modern CI/CD tools generate logs and metrics at every stage including commits, builds, tests, and deployments. With the right observability platform, these logs become actionable insights that keep your pipelines healthy.

Things to Consider When Developing CI/CD Pipelines

Building a CI/CD pipeline sounds straightforward until you’re knee-deep in automation scripts, failing builds, and unpredictable tests. To make your pipeline efficient and reliable, you need to focus on the right priorities from the start.

#1 Avoid Automating the Wrong Processes

Automation should save time, not create unnecessary complexity. Automating trivial or low-frequency tasks can do more harm than good.

Ask yourself:

• How long does this task take manually?
• Does automation meaningfully improve speed or quality?
• Will this process scale as the team grows?

Keep automation focused on repeatable, time-consuming, and error-prone steps such as builds, tests, and deployments.

#2 Ensure Effective Testing

To ensure effective testing, teams need clear visibility into how tests perform across environments and builds. This includes tracking pass/fail rates, identifying bottlenecks, and understanding trends over time. Leveraging detailed test reporting helps teams analyze results faster, improve coverage, and make informed decisions about release readiness.

#3 Build Observability into the Pipeline

Without observability, diagnosing slow builds or failed deployments turns into guesswork. Integrate tools that provide visibility into each stage, from commit to production, so you can trace performance bottlenecks and identify issues immediately.

Observability in CI/CD Pipelines

Modern CI/CD pipelines are complex ecosystems where multiple stages, tools, and environments work together in real time. When one component fails, it can trigger cascading issues unless you have clear visibility into what’s happening. Observability provides that visibility. It helps you understand not just what failed, but why it failed.

Building an Observable CI/CD Pipeline

An observable pipeline collects, correlates, and visualizes three core data types:

• Metrics: Quantitative data such as build times, success rates, and test duration.
• Logs: Text-based records of events and errors generated by tools like Jenkins, CircleCI, or GitHub Actions.
• Traces: Transaction-level insights that show how data flows through the pipeline.

When combined, these signals create a complete view of pipeline health and performance.

Key CI/CD Observability Metrics

Tracking these metrics over time exposes bottlenecks, highlights inefficiencies, and shows exactly where you can optimize for better speed and quality.

Strengthening Security in the CI/CD Pipeline

Automation doesn’t just improve speed; it’s also your first line of defense against human error. A secure CI/CD pipeline ensures that only trusted code is deployed, credentials remain protected, and environments stay tamper-proof.

Key Security Layers in CI/CD

• Access Control: Apply least-privilege permissions to limit who can trigger builds or modify deployment scripts. This reduces the risk of unauthorized changes and insider threats.
• Routine Scanning: Automate security scans at every stage of the pipeline to detect vulnerabilities early. This includes dependency checks, container image scanning, and code analysis.
• Continuous Monitoring: Track configuration drift, key exposure, and suspicious activity in real time. Continuous monitoring helps teams identify and respond to threats before they escalate.

Automating these security layers eliminates unnecessary manual intervention and minimizes the risk of human mistakes that could lead to breaches.

Scanning Methods for Securing Pipelines

Security scanning should be built directly into the CI/CD pipeline, not treated as a separate phase. Integrating it early ensures vulnerabilities are caught before deployment, not after. Here are the essential types of scans every DevOps team should include.

#1 Container Scanning
Containers often rely on public images from registries such as Docker Hub. Each layer should be scanned for outdated dependencies, misconfigurations, and known vulnerabilities before deployment. This prevents compromised images from reaching production.

#2 Infrastructure Scanning
Use automated tools to validate that infrastructure configurations align with internal security policies. Confirm that encryption is enabled, access ports are properly secured, and all data is transmitted safely. These checks protect against misconfigurations that could expose systems to risk.

#3 Source Code Scanning
Static analysis tools can detect vulnerabilities such as:

• SQL injection
• Cross-site scripting (XSS)
• Hardcoded credentials or tokens

All secrets should be encrypted or managed through an external secrets manager to prevent accidental leaks or unauthorized access. By embedding these scans into your CI/CD pipeline, every build is verified, hardened, and deployed with security as a default rather than an afterthought.

Keep Pipelines Secure with Continuous Monitoring and Auditing

Your CI/CD pipeline is just as critical and vulnerable as your production environment. Monitoring and auditing pipeline activity are essential to protect against unauthorized changes, misconfigurations, and credential compromise.

Best Practices for Pipeline Security Monitoring

• Rotate access keys regularly and revoke any unused credentials to limit exposure.
• Track every pipeline change through detailed audit logs so you can identify who made what change and when.
• Use monitoring tools to detect unusual patterns or behaviors and trigger alerts for suspicious activity.
• Implement automated remediation to respond immediately such as disabling compromised accounts or removing outdated permissions.

Security is not a one-time effort. Continuous monitoring and auditing ensure that your CI/CD pipelines remain secure, resilient, and compliant as your systems grow and evolve.

Conclusion

CI/CD pipelines are no longer just a development convenience; they’ve become mission-critical infrastructure. Integrating observability transforms your pipeline into a continuous feedback system that delivers real-time insights and control.

When visibility, monitoring, and security work together, you not only move faster but also make better decisions. Teams can detect issues early, optimize every stage of delivery, and protect the software supply chain from failures or attacks.

Observability should never be an afterthought. It must be built into the CI/CD architecture from the beginning so your teams can deploy with confidence, every single time.

FAQS

1) What is a CI/CD pipeline and why is it important?

A CI/CD pipeline automates the process of building, testing, and deploying code. It’s critical because it enables teams to release software faster, reduce human error, and maintain consistent quality across environments.

2) What are the main stages of a CI/CD pipeline?

The primary stages include code commit, build, testing, deployment, and monitoring. Each step ensures that software moves smoothly from development to production without manual intervention or downtime.

3) Why should I monitor my CI/CD pipeline?

Monitoring ensures your pipeline runs smoothly and alerts you when builds fail, tests break, or deployments slow down. It helps identify bottlenecks, prevent outages, and maintain high release velocity.

4) What is observability in a CI/CD pipeline?

Observability provides visibility into every part of the CI/CD process using metrics, logs, and traces. It allows teams to detect, understand, and resolve issues faster improving reliability and performance.

5) How do I secure my CI/CD pipeline?

Securing your CI/CD pipeline involves access control, automated security scanning (for code, containers, and infrastructure), regular key rotation, and continuous monitoring to prevent vulnerabilities.

6) How does Atatus help with CI/CD pipeline monitoring?

Atatus offers a unified observability platform that tracks metrics, traces, and logs across your entire stack. It helps you identify pipeline issues, optimize performance, and secure your deployments.

In microservices and cloud-native environments, vulnerabilities buried in transitive dependencies or runtime behaviors can go undetected for weeks. During that time, your attack surface keeps expanding and production systems remain exposed. The longer remediation is delayed, the greater the risk of exploitation, compliance failures, and operational disruption.

In this post, we walk you through how Atatus Vulnerability Management handles the entire remediation workflow, from precise detection to secure deployment, enabling teams to resolve risks up to 3× faster.

1. Step 1: Detect with Runtime Precision
2. Step 2: Review Vulnerability Details and Guidance
3. Step 3: Prioritize with Contextual Intelligence
4. Step 4: Get Root Dependency Insights Before Fixing
5. Step 5: Fix Vulnerabilities at the Dependency Level
6. Step 6: Verify and Monitor Post-Remediation

Step 1: Detect with Runtime Precision

Atatus vulnerability management continuously scans your full stack, applications, hosts, and cloud assets, without disrupting performance. It identifies CVEs in third-party libraries, OS packages, Docker images, and IaC configs using feeds like NVD, EPSS, and threat intel.

Unlike static scanners, Atatus correlates findings with APM traces and logs to confirm whether a vulnerability is actively reachable. The dashboard surfaces every detail clearly, library name, version, CVE ID, CWE, EPSS score, affected service, and exposure window, so teams know exactly what they are dealing with.

Step 2: Review Vulnerability Details and Guidance

Each vulnerability card presents the full picture, CVSS score, EPSS probability, affected library and version, CVE ID, CWE classification, impacted service, and environment context. Teams also get a precise timeline: first detected date, last detected date, window of exposure, and CVE published date.

The Remediation section makes the fix clear and direct. It tells you exactly which version to upgrade to, with no guesswork involved. The severity breakdown shows whether a public exploit is available and the exploitation probability, helping teams assess real-world risk before acting.

Step 3: Prioritize with Contextual Intelligence

Not every vulnerability needs immediate attention. Atatus auto-groups vulnerabilities by root dependency, so fixing one outdated library can resolve multiple transitive CVEs at once.

Atatus prioritizes vulnerabilities by weighing exploitability, runtime exposure, and blast radius across microservices, so teams always know what to fix first.

Step 4: Get Root Dependency Insights Before Fixing

Upgrades carry risk. Before acting, Atatus surfaces key metadata including publish date, license changes, maintainer health, and impact forecasts. The Catalogs view lists all libraries in use across your services, flagging outdated versions, license types, and vulnerability counts per library. Teams can filter by service, language, license, and version status for complete visibility.

Step 5: Fix Vulnerabilities at the Dependency Level

Once a vulnerability is reviewed, teams can trigger remediation directly from the dashboard. Atatus provides the exact upgrade path for your package manager, so you can apply the fix directly without manually tracking down the right version. The suggested upgrade is clearly stated within the vulnerability detail, making the action straightforward for any team member.

CI/CD plugins embed scans into every build, and IDE alerts via the VS Code extension keep developers informed early in the development cycle, shifting security left without adding overhead.

Step 6: Verify and Monitor Post-Remediation

Once a fix is deployed, Atatus runs automated post-deploy re-scans to confirm resolution. The dashboard updates with a Resolved status, and teams can track patch coverage and MTTR trends over time.

Ongoing monitoring flags regressions immediately if a new vulnerability appears in a previously fixed dependency. Compliance reports for SOC2 and HIPAA audits can be exported with full vulnerability history and risk reduction summaries.

Context is the Key to Effective Vulnerability Management

Transitive vulnerabilities are inevitable in modern software. A single outdated root dependency can introduce dozens of CVEs across multiple services without a single line of your own code changing. The real challenge is not finding them. It is having enough context to fix them safely, knowing which service is actively running the affected library, what the precise upgrade version is, and what downstream impact that change will have.

That context is what separates a vulnerability list from an actionable remediation workflow. When CVE data is tied to runtime services, CVSS v4 scores, EPSS probability, CWE classification, and exact upgrade paths, teams stop guessing and start shipping fixes with confidence. Post-deployment re-scans close the loop, confirming resolution and keeping patch coverage measurable over time.

Without this level of visibility, critical exposure windows stay open longer than they should. And as the Ascension breach showed, the cost of delayed remediation goes far beyond a technical debt conversation.

Are you concerned about the security of your website or online business? Do you want to ensure that your customers can trust your site and transact with you safely? If so, then you need to know about SSL certificate monitoring!

SSL certificate monitoring is the process of continuously monitoring SSL certificates for potential vulnerabilities or incidents, such as certificate revocation or expiration, and other security issues.

By monitoring SSL certificates in real-time, website owners can detect and resolve issues before they cause website downtime or security breaches.

In this blog, we will dive deep into the world of SSL certificate monitoring, discussing its importance and benefits, the different types of SSL certificates, and how to effectively monitor SSL certificates.

Let's get started!

Table of Contents

1. What are SSL Certificates?
2. What is SSL Certificate Monitoring?
3. Why is SSL Certificate Monitoring Important?
4. How does SSL Certificate Monitoring Work?
5. What is SSL Certificate Incident?
6. How to monitor SSL certificates using Atatus?
7. Benefits of SSL Monitoring

What are SSL Certificates?

An SSL (Secure Sockets Layer) certificate is a digital certificate that provides a secure and encrypted connection between a web server and a user's browser. SSL certificates are used to protect sensitive information, such as login credentials, credit card numbers, and other personal information during online transactions.

An SSL certificate contains information about the website, such as the domain name, the company or organization that owns the website, and the expiration date of the certificate. The certificate also includes a public key, which is used to encrypt data sent between the user's browser and the web server.

When a user visits a website with an SSL certificate, their browser checks the certificate to ensure it is valid and issued by a trusted Certificate Authority (CA). If the certificate is valid, the browser establishes an encrypted connection with the web server, which protects the user's information from being intercepted by hackers or other third parties.

The SSL protocol has been replaced by the more secure TLS (Transport Layer Security) protocol, but the term SSL certificate is still widely used to refer to both SSL and TLS certificates.

Types of SSL Certificates

There are several types of SSL (Secure Sockets Layer) certificates available, each designed to meet different security and business needs. Here are some of the most common types of SSL certificates:

1. Domain Validated SSL certificate

A Domain Validated SSL certificate is the most basic type of SSL certificate. It verifies only that the person or organization requesting the certificate has control over the domain name associated with the SSL certificate. They provide the least amount of assurance to website visitors since they do not verify the identity of the organization or individual that owns the website. As a result, DV SSL certificates are best suited for personal websites or small blogs that do not deal with sensitive information.

2. Organization Validated (OV) SSL Certificates

Organisation Validated SSL certificates provide a higher level of authentication than Domain Validated SSL certificates by requiring additional validation steps. Monitoring Organisation Validated SSL certificates involves checking their expiration date, certificate chain, and verifying the organization details associated with the certificate.

3. Extended Validation SSL Certificates

EV SSL certificates are the most advanced type of SSL certificate. They provide the highest level of security by verifying not only the domain name and organization information, but also conducting a more rigorous validation process to ensure the legitimacy of the website. EV certificates are recognized by the green address bar and are recommended for large organizations, e-commerce websites, and financial institutions.

4. Multi-Domain SSL Certificates

Multi-Domain SSL certificates can cover multiple domain names, making them a cost-effective solution for websites with multiple domains. Monitoring Multi-Domain SSL certificates involves checking their expiration date, certificate chain, and verifying the domain names associated with the certificate.

5. Wildcard SSL Certificate

A wildcard SSL certificate is a type of SSL certificate that allows you to secure a main domain and all its subdomains with a single certificate. The certificate is issued to .domain.com, where the asterisk () is a wildcard character that can match any subdomain.

For example, if you have a website with the domain name example.com, a wildcard SSL certificate for *.example.com would secure all subdomains, such as mail.example.com, shop.example.com, and blog.example.com.

6. Unified Communications (UC) SSL Certificate

UC SSL certificates are commonly used in Unified Communications (UC) environments, where multiple communication services, such as email, voice, video conferencing, instant messaging, and collaboration tools, are integrated into a single platform. These certificates allow for secure communication between different services within the same UC environment.

Unlike a Wildcard SSL certificate, which secures all subdomains of a single domain, a UC SSL certificate can secure multiple, unrelated domains and hostnames. For example, a UC SSL certificate can secure mail.example.com, voip.example.com, and collaboration.example.com, as well as the IP addresses associated with these services.

What is SSL Certificate Monitoring?

SSL certificate monitoring is the process of regularly monitoring and verifying the validity and security of SSL certificates on a website. SSL certificates are digital certificates that enable secure communication between a web server and a web browser, ensuring that sensitive information are transmitted securely over the internet.

When you visit a website that requires you to enter sensitive information like your credit card number or personal details, you want to make sure that your information is transmitted securely and cannot be intercepted by hackers.

SSL certificates help ensure the security of the website by encrypting the information that is being transmitted between the website and your device.

Automated vs Manual SSL Certificate Monitoring

Manual monitoring involves checking the SSL certificate status periodically, either by visiting the website and checking the certificate information or by using online SSL certificate checker tools. This process can be time-consuming and requires human effort, especially if you have multiple websites to monitor.

Automatic SSL certificate monitoring, on the other hand, uses specialized tools and services that regularly check the SSL certificate status and send alerts if any issues are detected. These tools can also automate the renewal and installation process of SSL certificates, making it a more efficient and hands-off process. However, this method can be more expensive.

However, the best choice depends on the specific needs of your website and your available resources. If you have a small website with only a few pages and limited budget, manual monitoring may be sufficient. if you have a larger website with many pages and sensitive data to protect, automated SSL certificate monitoring is the more reliable and efficient option.

Why do SSL Certificates Expire?

1. SSL certificates expire to ensure the continued security of websites and their users.
2. SSL certificates contain information about the website owner's identity and the certificate's validity.
3. When an SSL certificate expires, the information contained in the certificate is no longer considered reliable.
4. SSL certificates typically expire after a certain period of time, such as one year or two years.
5. Website owners must periodically renew their SSL certificates to ensure their website remains secure and the information transmitted between the server and the client remains encrypted.
6. Failure to renew an SSL certificate can result in sensitive information such as passwords, credit card numbers, and other personal data being exposed to potential attackers.
7. SSL certificate expiration can also ensure website owners regularly review and update their security practices.

Why is SSL Monitoring Important?

SSL monitoring is essential for several reasons:

1. Security: SSL monitoring helps to ensure the security of websites and web applications by detecting and reporting potential security threats, such as malware and phishing attacks.
2. Compliance: SSL monitoring is essential for compliance with regulatory requirements, such as PCI DSS and HIPAA, which require using SSL to protect sensitive data.
3. Performance: SSL monitoring can help to optimize the performance of SSL-enabled applications by identifying and resolving issues related to SSL certificate expiration, configuration errors, and other issues that can cause slow page load times and other performance problems.
4. Visibility: SSL monitoring provides visibility into SSL traffic, enabling network administrators to quickly identify and troubleshoot SSL-related issues.
5. Mitigation: SSL monitoring helps organizations quickly mitigate SSL-based attacks, such as man-in-the-middle (MITM) attacks, by identifying and blocking the attack's source.

How does SSL Monitoring Work?

The SSL certificate is installed on the web server, and a chain of trust is established between the certificate and the certificate authority. The SSL certificate monitoring system is set up to send automated HTTP requests at a pre-defined frequency to the specified URL.

When an HTTP request is sent to the URL, the web server responds with its SSL certificate. The monitoring system checks the SSL certificate to ensure that it's valid and has been issued by a trusted CA. The SSL certificate is also checked for its expiration date to ensure that it hasn't expired.

If the SSL certificate is found to be invalid or has expired, the monitoring system starts an SSL certificate incident, which alerts the relevant team or person. The monitoring system sends alerts, typically via email or SMS, to the relevant person or team on the on-call calendar. The alert message will contain details about the SSL certificate issue, such as the reason for the incident and the URL that is affected.

Once an alert has been received, the relevant team or person can troubleshoot the issue by identifying the root cause of the problem and taking appropriate actions. Once the SSL certificate has been renewed and the configurations have been updated, the SSL certificate incident is resolved, and the monitoring system stops alerting.

In practice, this process becomes significantly easier with automated SSL certificate discovery, which helps teams continuously identify certificates across environments and monitor them before they expire. Platforms like TigerTrust simplify this by providing real-time visibility and proactive alerts.

How to monitor SSL certificates using Atatus?

You can monitor SSL certificates using Atatus monitoring tool by following these steps:

Step 1: Log in to your Atatus account and navigate to the "Synthetic" tab.

Step 2: Click on "New check" in the right corner of the dashboard and choose "New API/Web Check".

Step 3: Choose Request Type as SSL.

Step 4: Enter the host name. The default port number is set to 443, but it can also be changed. Enter the API Check name to identify the specific check you are performing, such as "SSL API Check". Optionally, add tags to the API check to group it with other related checks or to filter results based on specific criteria.

Step 5: Based on the request type you select, there will be some predefined assertions. Besides that, you can also create custom assertions by clicking on the "Add assertion button", which helps you to determine whether a check has been successful.

Step 6: You can either select all location or you can choose any particular location to check from the given location.

Step 7: Select the run type

1. Parallel: Runs the check on all locations parallely.
2. Sequential: Runs the check at each location specifically.

Step 8: Specify the test frequency on how often the test should be performed.

Step 9: Define the alert conditions based on the number of retries to be done when an failure occurs.

Step 10: Set up the notification channel and notify your team about the expiration of the SSL certificate.

Step 11: Click on "Create New Check" and start monitoring your SSL certificate.

What is SSL Certificate Incident?

An SSL certificate incident refers to a situation where a website's SSL certificate has expired, become invalid, or has been revoked. It triggers an automated monitoring system to alert the website owner or IT team responsible for website security.

The alert notifies them that the SSL certificate is no longer valid and needs to be renewed. Failure to address an SSL certificate incident promptly can lead to website unavailability or visitor's personal information being compromised.

It is essential to take SSL certificate incidents seriously and address them immediately to ensure website security and availability.

How to Receive SSL Certificate Incident Alerts?

To receive SSL certificate incident alerts, you can set up automated monitoring tools that periodically check your website's SSL certificate status. These tools can send alerts via email, SMS, or other messaging channels to notify you when an SSL certificate incident occurs.

It is essential to configure SSL certificate monitoring tools to check the validity of SSL certificates regularly, such as every 24 hours or more frequently, depending on the website's security needs. It is also crucial to ensure that the alerts go to the right individuals or teams to ensure prompt resolution of the incident.

Incident Resolution Process

The incident resolution process for SSL certificate monitoring typically involves the following steps:

1. The first step is to identify the issue and determine the scope of the incident.
2. Once the incident has been identified, the next step is to analyse it and determine the root cause.
3. The affected system or service is then isolated or taken offline to prevent further damage or compromise.
4. Mitigation steps are taken to address the issue and prevent it from recurring. This may involve renewing or replacing the SSL certificate or taking other actions to secure the system.
5. Once the issue has been mitigated, the system or service is restored to its normal state. Finally, a post-incident review is conducted to evaluate the effectiveness of the response and identify opportunities for improvement.

Benefits of SSL Monitoring

1. SSL Certificate Monitoring allows for early detection of potential issues before they result in downtime or security breaches.
2. It improves website and application availability, ensuring that any SSL issues are promptly resolved.
3. Customers can trust the website or application more knowing that SSL issues are promptly resolved, preventing any negative impact on their experience.
4. SSL certificate monitoring helps to mitigate the risk of financial loss caused by website downtime or security breaches.
5. SSL certificate incidents can be addressed more quickly with SSL certificate monitoring, as alerts are sent in real-time to the relevant stakeholders, resulting in faster response times.

Conclusion

SSL certificate monitoring is a crucial element of website security that demands attention. It allows website owners to constantly scan SSL certificates for potential vulnerabilities, enabling them to improve incident response time, increase their security posture, and build customer trust.

SSL monitoring can also mitigate the risk of financial loss arising from website downtime or security breaches. Although SSL certificate monitoring has some disadvantages such as cost, false alarms, and complexity, the advantages outweigh them.

Overall, SSL monitoring is a valuable investment in website security that can help protect businesses and their customers from potential harm. In today's highly competitive business landscape, website downtime or security breaches can lead to significant financial and reputational damage. Therefore, investing in SSL certificate monitoring can be a wise decision for businesses of all sizes.

Monitor your website using Atatus Synthetic Monitoring

Atatus Synthetic Monitoring ensures uptime, identifies regional issues, tracks application performance, and manages SLAs and SLOs by monitoring your applications, API endpoints, and critical business flows via simulated user requests.

Atatus monitors the performance of your webpages and APIs in a regulated and steady manner, from the backend to the frontend, and at multiple network levels, warning you in the event of erroneous behavior such as regression, broken feature, high response time, unexpected status code, and so on.

Try Atatus Synthetic Monitoring free for 14 days.

Dynatrace and Grafana are popular tools when it comes to monitoring and observability. In this post, I have compared Dynatrace and Grafana on important features like APM, log management, infrastructure monitoring, pricing, etc.

Table of Contents:

1. Dynatrace vs Grafana: An Overview
2. Real Pain Points to Watch Out For
3. APM: Which Catches Problems Faster?
4. Log Management: Speed vs. Flexibility
5. Infrastructure Monitoring
6. Visualization and Dashboards
7. UI/UX - Grafana vs Dynatrace
8. Pricing: What You’ll Actually Pay
9. Dynatrace vs Grafana: Final Verdict
10. Atatus - A better Alternative for Dynatrace and Grafana

Dynatrace vs Grafana: An Overview

Dynatrace is an enterprise-grade, AI-powered observability platform. It monitors everything like applications, infrastructure, logs, and security from a single agent. You install OneAgent, and it starts collecting data automatically. No stitching required.

Grafana is a visualization and dashboarding tool. Out of the box, it shows you nothing; it just connects to data sources. Pair it with Prometheus for metrics, Loki for logs, and Tempo for traces, and you have a powerful (but complex) open-source stack.

The core tension: Dynatrace is powerful but expensive. Grafana is flexible but requires significant engineering time to set up and maintain. Neither is obviously better. It depends on your team, your budget, and how much you want to manage.

Here’s a quick comparison of Dynatrace and Grafana based on key features and functionality:

✅ - Available  |  ❌ - Not Available | 🟡 - Limited

Real Pain Points to Watch Out For

Dynatrace Pain Points

• Sticker shock at renewal. Dynatrace bills on host units, DDUs (Davis Data Units), and a dozen other consumption metrics. Bills often come in 3–5x higher than expected.
• Hard to navigate. The UI has improved, but finding APM settings still often requires reading docs. Steep learning curve for new engineers.
• Overkill for small teams. If you’re running a 10-person SaaS startup, you’re paying for enterprise capabilities you’ll never use.

Grafana Pain Points

• It’s not one tool, it’s a stack. Grafana alone doesn’t monitor anything. You’re assembling Prometheus + Loki + Tempo + alert manager + exporters. Each needs maintenance.
• Dashboard sprawl. Teams end up with 50+ dashboards, half of them outdated. Without discipline, it becomes impossible to know which one to trust during an incident.
• No AI-assisted root cause. When something breaks at 2am, you’re manually combing through metrics and logs. There’s no Davis AI to point you in the right direction.
• Hidden ops cost. Self-hosting Grafana + Prometheus at scale means managing storage, retention policies, and high-availability setups. It’s not free in engineering time.

APM: Which Catches Problems Faster?

To evaluate the APM feature in Grafana and Dynatrace, I sent data from a sample application to both platforms. While the APM setup was straightforward on both, Grafana required a few extra steps.

Grafana APM

Grafana Cloud uses its Application Observability solution, integrating with Tempo (traces), Prometheus (metrics), and Loki (logs). It works well, but requires you to set it up, instrument your code, and configure your pipelines first.

For teams comfortable with OpenTelemetry, Grafana is a solid, flexible choice. For teams that want to plug something in and have it just work, it’s more of a project.

Dynatrace APM

Install OneAgent, and Dynatrace starts monitoring your application automatically. It maps service dependencies, traces transactions end-to-end and this is the key differentiator, uses its Davis AI to tell you what broke and why, not just that something broke.

One caveat: finding APM in the Dynatrace UI isn’t obvious. New users often miss it entirely without checking the docs. That’s a real friction point.

Pros: Zero-code auto-instrumentation, AI detects and explains anomalies, full-stack context (code to infra), automatic dependency mapping.

Cons: Hard to find in the UI, OneAgent SDK needs code changes, expensive at scale.

If you want automated, AI-powered problem detection with minimal effort, Dynatrace wins. If you want flexibility and control and have the engineering bandwidth, Grafana is viable.

Log Management: Speed vs. Flexibility

Grafana Loki

Loki indexes only metadata labels (not full log content), keeping costs low for high-volume ingestion. It accepts logs without requiring a specific format, genuinely useful for polyglot stacks. It supports live tailing and can generate metrics and alerts from log data.

The tradeoff: LogQL has a learning curve, and without full-text indexing, some complex log searches are slower than you’d like during a production incident.

Dynatrace

On the other hand, Dynatrace uses Grail for log management, which is designed to handle log data in a more schema-less way. Grail allows users to store massive amounts of log data (even in petabytes) without requiring traditional indexing. Once OneAgent is deployed, it seamlessly ingests logs into the Dynatrace platform.

Users have the flexibility to define custom ingestion rules and manage log retention according to specific business or compliance requirements. This makes Dynatrace's log management particularly useful for scenarios like debugging or auditing, where the ability to store and query logs over long periods is essential.

Loki is better for cost-conscious teams who want flexibility. Dynatrace Grail is better for teams that need deep APM-to-log correlation and don’t want to configure anything manually.

Infrastructure Monitoring

Grafana Cloud provides infrastructure monitoring for various environments like AWS, Kubernetes, and other integrations such as Linux machines, databases, and applications. With a managed Grafana instance, it offers integrations with diverse data sources, robust visualization tools, and alerting capabilities.

This made it easy for me to focus on monitoring my infrastructure without worrying about managing the backend. I was also able to build customized dashboards to track specific infrastructure metrics.

On the other hand, Dynatrace offers a single unified view of infrastructure metrics. As I navigated through the platform, I could easily access all the key performance indicators I needed, including outgoing connections, host performance, process analysis, disk usage, and more.

The Host performance analysis provides detailed information on CPU usage, memory usage, traffic, and disk throughput. Depending on needs, additional information such as swap usage, disk latency, and disk space usage can also be accessed.

This in-depth details helped me better understand my system’s performance and made it easier to make decisions about resource optimization and management.

Dynatrace is the better choice for detailed infrastructure monitoring with automated insights and easy access to key metrics.

Visualization and Dashboards

Grafana is known for its excellent visualization capabilities. It offers a wide range of visualizations, including time series graphs, pie chart, bar charts, heat maps, and more.

It is highly customizable, allowing users to adjust panels and metrics. Grafana also suggests visualization types based on your data, making it easier to choose the best representation. It is ideal for those who value flexibility and customization.

Dynatrace provides good visualizations for monitoring application and infrastructure performance. It focuses on clear, comprehensive views of real-time and historical data but offers less customization than Grafana.

When comparing Dynatrace vs Grafana in terms of visualization capabilities, Grafana is the preferred choice for users seeking versatile visualizations.

UI/UX - Grafana vs Dynatrace

I found Dynatrace’s user interface to be difficult to use, as it lacked clarity and wasn’t intuitive. Navigating the platform often required checking the documentation, which made the overall experience less enjoyable.

In contrast, Grafana provides a visually appealing and powerful UI, especially when compared to Prometheus. Its interface is user-friendly and designed for easy navigation and interaction.

Grafana offer great visualization capabilities. If you need more customization or want to visualize data from multiple sources, choose Grafana.

Pricing: What You’ll Actually Pay

Grafana, as an open-source platform, is free to use. It also offers a cloud version and an enterprise version to meet different user needs. The Grafana Cloud edition includes a free forever plan, while the enterprise version comes with a 14-day free trial.

Dynatrace follows a pricing model that varies based on the services used. It offers a 15-day free trial, allowing users to explore the platform’s features.

Both tools provide free and paid plans, but if you are looking for a fully free self-managed solution, the Grafana open-source version is the best choice.

Dynatrace vs Grafana: Final Verdict

If you are trying to decide between Grafana and Dynatrace, it depends on your specific use case and monitoring needs. Here’s a breakdown of when each tool is the better choice:

When to Choose Grafana:

• Custom Dashboards & Visualization : If you need highly customizable dashboards with a wide range of data sources, Grafana is ideal.
• Cost-Effective Monitoring : Grafana, especially with Loki for logs and Prometheus for metrics, provides a budget-friendly open-source solution.
• Flexible Infrastructure Monitoring : It integrates seamlessly with cloud platforms like AWS, Kubernetes, and databases, making it great for distributed environments.

When to Choose Dynatrace:

• AI-Driven Automation : Dynatrace provides automatic anomaly detection and root cause analysis, reducing manual troubleshooting efforts.
• Comprehensive Observability : With APM, log management, infrastructure monitoring, and security in a single platform, it offers an all-in-one solution.
• Minimal Manual Setup : Unlike Grafana, which requires manual integration with data sources, Dynatrace’s OneAgent automates data collection and monitoring.

Still not sure? Atatus offers a balanced alternative with APM, log management, and infrastructure monitoring in a single, cost-effective platform.

Atatus - A better Alternative for Dynatrace and Grafana

Atatus is a full-stack application performance monitoring and observability tool that serves as an excellent Dynatrace and Grafana alternative.

Here are a few reasons why you can consider Atatus as an alternative to Dynatrace and Grafana:

• All in one platform: Atatus, as a full-stack monitoring solution, offers complete visibility into your logs, metrics, and traces from a single interface. It eliminates the need for additional tools or integrations, providing seamless monitoring for both your applications and infrastructure.
• Custom Dashboards: Atatus provides a user-friendly interface for creating custom dashboards to display metrics, alerts, and other data, making it easy for organizations.
• APM (Application Performance Monitoring): Identify and fix spikes in endpoint failures and monitor application performance in real-time. Gain in-depth insights into transaction details, slow database queries, poor-performing network calls, and more.
• Log Management: Atatus provides a flexible log management solution with an out-of-the-box UI. Correlate logs with APM traces and errors for a comprehensive view.
• Distributed Tracing: Gain end-to-end visibility into microservices, perform root cause analysis with trace data, map service dependencies, and correlate logs with traces for a detailed understanding of your system.

FAQs About Dynatrace and Grafana

What is the main difference between Dynatrace and Grafana?
Dynatrace is an all-in-one observability platform with automatic monitoring and AI-powered root cause analysis. Grafana is a visualization tool, it doesn't collect data on its own. You need to pair it with Prometheus, Loki, and Tempo to build a full monitoring stack. Dynatrace is easier to start with; Grafana gives you more control.

Is Grafana free to use?
Yes. The open-source version of Grafana is completely free to self-host. Grafana Cloud also has a free tier (50GB logs, 10k metric series, 50GB traces). Beyond that, you pay based on usage. Keep in mind that self-hosting has hidden costs, someone on your team has to set it up and keep it running.

Is Dynatrace worth the cost for small teams?
Honestly, probably not. Dynatrace is built for enterprise environments with large infrastructure and compliance needs. If you're a startup or a small SaaS team, you'll likely pay for capabilities you don't use. A lighter alternative like Atatus gives you APM, logs, and infrastructure monitoring at a fraction of the price.

Can Grafana replace Dynatrace?
It depends on what you need. Grafana can cover APM, logs, and infrastructure monitoring when combined with the right tools but you're assembling and maintaining that stack yourself. Dynatrace does all of it automatically, with AI. If your team has the bandwidth, Grafana can replace Dynatrace. If not, you'll feel the gap fast.

Which tool is easier to set up - Dynatrace or Grafana?
Dynatrace. You install OneAgent and it starts collecting data automatically within minutes. Grafana requires manual configuration of multiple tools, data sources, dashboards, and alert rules. For teams without a dedicated platform engineer, Grafana's setup can take days or weeks to get right.

Does Grafana have AI-powered monitoring like Dynatrace?
No. Dynatrace has Davis AI built in, it detects anomalies and tells you the root cause automatically. Grafana has no equivalent out of the box. When something breaks, you're doing the investigation yourself by digging through dashboards and logs manually.

What is a good alternative to both Dynatrace and Grafana?
If you want the automation of Dynatrace without the enterprise price tag, and the simplicity of a managed tool without Grafana's setup complexity, Atatus is worth trying. It covers APM, log management, infrastructure monitoring, real user monitoring, and distributed tracing in one platform with a 14-day free trial and no credit card required.

Splunk isn't bad software. It's genuinely powerful. But in 2026, a lot of engineering teams are asking a fair question: are we getting $300K worth of value out of this? More often than not, the answer is no.

We went through 15 alternatives - read the docs, tested where we could, and talked to engineers who made the switch. This is what we found.

What's in this article?

• Why 67% of Teams Are Moving Away from Splunk in 2026?
• What to Look for in a Splunk Alternative (2026 Buyer's Checklist)
• Top 15 Splunk Alternatives in 2026
• Why Atatus Is the #1 Splunk Alternative for 2026?

Why 67% of Teams Are Moving Away from Splunk in 2026?

Splunk built its dominance on a powerful proposition: index anything, search everything. But in 2026, that proposition comes at a price that's forcing engineering leaders to reconsider.

Here are the 4 most common reasons teams replace Splunk:

What to Look for in a Splunk Alternative (2026 Buyer's Checklist)

Before evaluating any tool, align your team on these 7 criteria:

Top 15 Splunk Alternatives in 2026

1. Atatus
2. Datadog
3. Dynatrace
4. New Relic
5. Elastic
6. Grafana Cloud
7. Sumo Logic
8. Graylog
9. IBM Instana
10. Honeycomb
11. SolarWinds Observability
12. Loggly
13. AWS CloudWatch
14. Fluentd
15. Mezmo

Ranked by overall value, coverage breadth, and buyer satisfaction. Tools 1–3 are our top recommendations for most engineering teams.

Atatus - ⭐ Best Overall Splunk Alternative

Overview

Atatus is a unified full-stack observability platform purpose-built for engineering teams who need correlated logs, traces, metrics, and real user data in one place, without the per-GB pricing shock. Unlike Splunk's siloed approach, Atatus connects application performance data with logs and infrastructure signals so engineers can debug issues end-to-end in minutes, not hours.

Key Features

• Unified APM, Logs, RUM, Synthetics, Infrastructure & Database monitoring
• Real-time log streaming with live tail, custom parsing rules, and facets
• Cross-signal correlation: jump from a log line directly to its APM trace
• AI-powered anomaly detection and intelligent alerting
• Custom dashboards, saved views, and role-based access control
• Long-term log storage with configurable retention policies
• OpenTelemetry-compatible data ingestion
• 14-day free trial, no credit card required

Pros & Cons

Best For

SaaS companies, e-commerce platforms, and engineering teams (10–500 developers) wanting full-stack observability at 60–70% lower cost than Splunk.

Pricing

Free 14-day trial. Paid plans start from an affordable base. Contact Atatus for custom quotes. No per-GB ingestion tax.

Datadog - Best for Cloud-Native Teams

2. Datadog  - Best for Cloud-Native Teams

Overview

Datadog is a cloud-native monitoring and analytics platform offering a wide breadth of integrations and telemetry signals. Popular with large engineering orgs already embedded in AWS/GCP/Azure ecosystems.

Key Features

• 700+ integrations across cloud, infra, and APM
• Centralized log management with 'Logging Without Limits'
• Cloud SIEM and security monitoring
• Real user monitoring (RUM) and synthetic testing
• AI-assisted incident management

Pros & Cons

Best For

Large cloud-native engineering orgs with dedicated SRE teams and budget flexibility.

Pricing

Infrastructure from ~$15/host/month. APM, logs, RUM all billed separately. Enterprise often $100K+/year.

Dynatrace - Best for AI-Driven Root Cause Analysis

Overview

Dynatrace offers AI-powered full-stack observability with its proprietary Davis AI engine for automatic root cause identification. Favored by large enterprises running complex microservices.

Key Features

• OneAgent auto-discovery and auto-instrumentation
• Davis AI for real-time root cause analysis
• Kubernetes-native monitoring
• Application security (RASP/SAST)
• Business analytics and SLO management

Pros & Cons

Best For

Enterprises with complex microservices architectures needing automated root cause analysis.

Pricing

From ~$350/host/year. Full platform with Davis AI significantly higher. Custom enterprise contracts.

New Relic - Best for Developer Experience

Overview

New Relic's unified observability platform offers a generous free tier and consumption-based pricing model that has attracted many teams migrating from Splunk. Strong code-level APM and error tracking make it a developer favorite.

Key Features

• Full-stack telemetry: metrics, events, logs, traces (MELT)
• Code-level distributed tracing and profiling
• Errors Inbox for developer-centric error workflows
• 700+ quickstarts and integrations
• AI/ML-powered alerting with predictive analytics

Pros & Cons

Best For

Developer-led teams and startups who want powerful observability with a generous free tier.

Pricing

Free tier: 100GB/month + 1 full user. Pro from ~$25/GB ingested + per-seat costs.

Elastic (ELK Stack) - Best Open-Source Option

Overview

The Elastic Stack (Elasticsearch, Logstash, Kibana, Beats) is the most popular open-source alternative to Splunk. Elastic Cloud provides a managed SaaS version for teams wanting managed infrastructure.

Key Features

• Elasticsearch for blazing-fast full-text search
• Kibana for visualization and dashboards
• Logstash/Beats for data ingestion pipelines
• APM agents available for major languages
• Elastic Security for SIEM and threat hunting

Pros & Cons

Best For

Engineering teams with DevOps expertise wanting flexible, search-driven log analysis without vendor lock-in.

Pricing

Self-hosted: free (OSS). Elastic Cloud from ~$95/month for small deployments; scales with data.

Grafana Cloud - Best for Open-Source Stack

Overview

Grafana Cloud bundles Grafana dashboards with Loki (logs), Tempo (traces), and Mimir (metrics) into a managed observability stack. Ideal for teams already using Prometheus and open-source tooling.

Key Features

• Grafana dashboards with extensive plugin ecosystem
• Loki for cost-efficient log aggregation (index-free)
• Tempo for distributed tracing
• Mimir for scalable metrics storage
• OpenTelemetry and Prometheus native support

Pros & Cons

Best For

Teams running Prometheus/Kubernetes-native stacks who want managed Grafana without self-hosting.

Pricing

Free tier available. Pro from ~$19/month + usage-based pricing for logs, metrics, traces.

Sumo Logic - Best for Security + Observability Combo

Overview

Sumo Logic is a cloud-native SIEM and log analytics platform that blends security and observability signals. Popular with compliance-heavy industries like fintech and healthcare.

Key Features

• Machine learning-driven analytics and anomaly detection
• Cloud SIEM with threat intelligence integrations
• Real-time dashboards and scheduled reports
• Kubernetes and containerized workload support
• Ingest and retention controls per tier

Pros & Cons

Best For

Security-conscious engineering and SecOps teams in regulated industries.

Pricing

Usage-based. Essentials tier from ~$3/GB ingested. Enterprise custom pricing.

Graylog - Best Budget-Friendly Open-Source

Overview

Graylog is a robust open-source log management platform with an enterprise tier. It offers centralized log collection, powerful search, and alerting without Splunk's licensing overhead.

Key Features

• Centralized log collection via Graylog Sidecar
• GELF (Graylog Extended Log Format) for structured logging
• Streams, pipelines, and rule-based routing
• Role-based access control and multi-tenancy
• Alerting via email, Slack, PagerDuty integrations

Pros & Cons

Best For

SMBs and IT teams needing log management without enterprise observability complexity.

Pricing

Open Source: free. Operations (cloud): from $1,350/month. Enterprise: custom.

IBM Instana - Best for Container & Microservices Monitoring

Overview

IBM Instana provides automatic, real-time observability for containerized and microservices environments. Its zero-configuration monitoring agent makes it a fast-deploy option for complex container fleets.

Key Features

• Automatic service discovery with 1-second granularity
• Continuous profiling and code-level trace analysis
• Smart alerting with contextual event grouping
• Kubernetes, Docker, and OpenShift native support
• Infrastructure dependency maps auto-generated

Pros & Cons

Best For

Enterprise teams running large Kubernetes/microservices environments needing automatic instrumentation.

Pricing

From $240/year per Managed Virtual Server (Essentials). Enterprise pricing on request.

Honeycomb - Best for High-Cardinality Debugging

Overview

Honeycomb is built for observability-driven development, enabling engineers to ask arbitrary questions of high-cardinality trace data without pre-defining metrics or dashboards.

Key Features

• High-cardinality event analysis at query time
• BubbleUp: visual outlier detection in trace data
• SLOs and error budget tracking built-in
• Trace-first debugging with wide event model
• Collaboration features for incident investigations

Pros & Cons

Best For

Product engineering teams focused on developer observability and trace-driven debugging.

Pricing

Free tier available. Pro from ~$130/month. Enterprise custom.

SolarWinds Observability - Best for Hybrid Stack Monitoring

Overview

SolarWinds Observability is a full-stack platform popular in mid-market and enterprise, offering unified visibility across applications, networks, databases, infrastructure, and digital experience.

Key Features

• Full-stack telemetry: metrics, traces, logs, digital experience
• Deep network and infrastructure observability
• OpenTelemetry and Telegraf/Prometheus support
• AI-assisted anomaly detection and guided diagnostics
• Guided cloud integration setup (AWS, Azure, GCP)

Pros & Cons

Best For

IT-ops-heavy enterprises with hybrid on-prem/cloud infrastructure needing deep network and infra visibility.

Pricing

From $144/year per network device or host. Enterprise pricing on request.

Loggly (by SolarWinds) - Best for Simple Cloud Log Management

Overview

Loggly is a cloud-based log management and analytics service ideal for SMBs needing quick setup without infrastructure complexity. It supports a wide range of log sources and offers simple, intuitive dashboards.

Key Features

• Cloud-hosted log aggregation — no infrastructure to manage
• Real-time log analysis and search
• Interactive dashboards and threshold alerting
• Log archiving and compliance reporting
• 150+ technology integrations

Pros & Cons

Best For

Small engineering teams and startups needing fast, simple log management.

Pricing

Lite: free (200MB/day). Standard from $79/month. Pro from $159/month.

AWS CloudWatch - Best for AWS-Only Environments

Overview

AWS CloudWatch is the native monitoring service for AWS workloads. For teams running exclusively on AWS, it offers deep, low-friction integration across all AWS services with no separate vendor to manage.

Key Features

• Native AWS service metrics and log ingestion
• CloudWatch Logs Insights for log querying
• Alarms, dashboards, and automated remediation
• Container Insights for ECS/EKS monitoring
• Serverless and Lambda monitoring built-in

Pros & Cons

Best For

Teams running 100% on AWS who want native monitoring without a third-party tool.

Pricing

Usage-based. Logs: $0.50/GB ingested; dashboards: $3/dashboard/month. Costs add up at volume.

Fluentd / Fluent Bit - Best Open-Source Log Collector

Overview

Fluentd and Fluent Bit are CNCF-graduated open-source log collectors that serve as the data pipeline layer — collecting, filtering, and routing logs to any backend (Elasticsearch, S3, Datadog, etc.).

Key Features

• 700+ plugins for input, filter, and output
• Lightweight Fluent Bit for resource-constrained environments
• JSON-unified log aggregation across sources
• Kubernetes DaemonSet deployment support
• Pluggable architecture with custom plugin support

Pros & Cons

Best For

Platform/DevOps teams building log pipelines as a component of a broader observability stack.

Pricing

Free and open-source. Cloud-managed options available via vendors like Calyptia.

Mezmo (formerly LogDNA) - Best for Pipeline-Driven Log Control

Overview

Mezmo offers log management with a strong emphasis on telemetry pipelines — giving engineering teams fine-grained control over log routing, transformation, and cost management before data lands in storage.

Key Features

• Telemetry pipeline with real-time transformation
• Auto-parsing of common log formats
• Role-based access and team workspaces
• Real-time alerting with PagerDuty/Slack integrations
• Log archiving to S3, Google Cloud Storage

Pros & Cons

Best For

Teams needing intelligent log routing and cost governance across multiple environments.

Pricing

Usage-based. Contact Mezmo for current pricing.

Why Atatus Is the #1 Splunk Alternative for 2026?

Every tool on this list solves part of the Splunk problem. Atatus solves all of it.

1. Unified Observability - One Platform for Everything

Most Splunk replacements solve one problem: better log management, or cheaper metrics, or smarter APM. Atatus delivers all of it in a single, correlated platform. APM, Logs, Real User Monitoring, Synthetic Monitoring, Infrastructure Monitoring, Database Monitoring, and API Analytics — all connected, all searchable from a single dashboard.

2. Faster Debugging - From Symptom to Root Cause in Minutes

Atatus creates a direct bridge between your log data and your application traces. When an error fires,  you will see the full APM trace, the user session, and the infrastructure state that caused it. Engineers using Atatus report 60–70% faster mean time to resolution (MTTR) vs. their previous Splunk setup.

3. Radically Better Cost Efficiency

Splunk's per-GB ingestion model means your monitoring costs scale with your success. Atatus uses predictable, team-friendly pricing that doesn't punish you for having a high-traffic application. Teams consistently report 60–80% cost savings vs. Splunk, with more signal coverage included.

4. Production-Ready in 30 Minutes

Splunk deployments take weeks of configuration, SPL training, and data pipeline setup. Atatus deploys in under 30 minutes with auto-instrumentation for all major languages and frameworks. Your team is solving problems, not configuring pipelines.

5. Built for Modern Engineering Teams

Atatus is designed for the stack engineers actually use in 2026: Node.js, Python, Go, Java, Rails, PHP, React, Angular, Kubernetes, AWS, GCP, Azure. It ships with OpenTelemetry support, Kubernetes monitoring, serverless observability, and real user monitoring out of the box.

Questions we get asked most

What's the cheapest Splunk alternative?

Depends on your definition of cheap. For self-hosted with no licensing fees, Graylog or the ELK Stack. For managed cloud with a real free tier, Grafana Cloud or New Relic. For the best cost-per-signal ratio in a paid tool, meaning the most coverage for the money. Atatus consistently comes out ahead of Datadog and Splunk in the teams we've talked to.

How long does migrating from Splunk take?

For most teams moving to Atatus, the initial instrumentation takes under 30 minutes. Data parity validation takes a few days of parallel running. A clean cutover with confidence typically happens within 1–2 weeks. More complex setups with custom Splunk apps take longer, but our team works through it alongside you.

Does Atatus support OpenTelemetry?

Yes, natively. If you're already running OTel collectors, you can route data directly to Atatus without re-instrumenting anything. It's one of the things teams mention most often when they describe why the migration went smoothly.

By incorporating observability tools into your stack, you can better understand how your complex infrastructure operates, reduce downtime, and empower developers to identify and fix problems quickly.

However, it now takes considerably more work, time, and money to build the best observability tools for your infrastructure and applications. According to a Splunk survey, over half of the firms polled employ eight or more observability tools.

Emerging solutions can meet your demands regardless of whether you are utilizing a microservices-based architecture, Kubernetes, a public cloud, hardware, networking, or a traditional IT stack. In this article, we'll learn about a few of them.

We will go over the following:

1. Atatus
2. AppOptics
3. Datadog
4. ManageEngine
5. Site24x7
6. Splunk
7. AppDynamics
8. Dynatrace
9. LogicMonitor
10. Monitis
11. Nagios
12. New Relic
13. Pingdom
14. Sumo Logic
15. UptimeRobot
16. Airbrake
17. Better Uptime
18. Rollbar
19. Logmatic
20. Sentry
21. Grafana
22. Instana
23. AppNeta
24. Epsagon
25. Prometheus
26. Honeycomb
27. Sematext
28. Icinga
29. Zabbix
30. Checkmk
31. Netdata
32. Observium
33. PRTG Network Monitor

#1 Atatus

Atatus is a SaaS-delivered application performance and error-tracking observability tool that offers full-stack visibility for all of your applications.

The platform can dynamically collect millions of performance data points from across your applications, enabling you to resolve issues more rapidly and enhance the digital user experience. With the flexibility of cloud or on-premise deployment, everything takes place in the field in real time.

Atatus ensures that your teams thoroughly understand all the performance metrics required to produce high-quality and dependable software. As soon as a problem arises, you can find out who, when, and what caused the performance bottleneck.

Atatus offers you various features to track the functionality of your apps and send you notifications when there are problems with the user experience. It's among the top choices in observability tools and microservices monitoring tools, providing invaluable insights into your system's health and performance.

#2 AppOptics

AppOptics is a SaaS-based, next-generation application performance management (APM) observability tool from SolarWinds. With distributed tracing, over 150 integrations, and custom metrics that all feed into the same dashboarding, analytics, and alerting pipelines, AppOptics is a seamless application and infrastructure microservices monitoring solution that was created to address the issues associated with separate monitoring and observability tools.

#3 Datadog

DataDog is a SaaS-based infrastructure monitoring and observability tool with many integrations. It enables DevOps teams to monitor evolving cloud native observability environments. This makes it simple to see the overall condition of your infrastructure by location, application, or service.

The DataDog agent is ideal for users with cloud or hybrid infrastructures since it can run on cloud platforms, bare metal servers, virtual machines, containers, and more.

#4 ManageEngine

ManageEngine Applications Manager is a server and application performance monitoring and observability tool offering complete insight down the line of code. For all server and application monitoring tool requirements, from server and infrastructure components that support a business to business-critical applications responsible for service delivery, the package offers integrated application performance monitoring.

In terms of cloud applications, application servers, web services, and other areas, ManageEngine Applications Manager supports over 100 major technologies.

#5 Site24x7

Site24x7 is a complete stack provider of performance monitoring and observability tools. Its microservices monitoring tool features extend to assessing the user experience of actual people visiting websites and applications from desktop and mobile devices.

It provides cloud monitoring for DevOps and IT operations. The business supports DevOps teams in monitoring and troubleshooting network observability tools infrastructure, including private cloud and public cloud, as well as servers, applications, and servers.

#6 Splunk

Splunk is the only full-stack, analytics-powered, and OpenTelemetry-native observability tool for looking up, tracking, and analyzing machine-produced data. Whether you use packaged, on-premises applications or cloud-native observability web applications, Splunk provides end-to-end insight across your stack.

The platform offers a high-level view of the functionality and efficiency of applications and services. Additionally, it enables users to conduct more thorough investigations to aid outage prediction and quickly identify a problem's main cause.

#7 AppDynamics

AppDynamics is a set of application performance monitoring and observability tools that enables users to improve employee and consumer digital experiences. The applications, which are developed on an enterprise-grade platform, boost digital transformations to increase customer engagement and operational effectiveness.

The APM features of AppDynamics offer visibility into each line of code and crucial transactions within multi-cloud setups. Other capabilities include business performance monitoring, infrastructure visibility, and end-user monitoring.

#8 Dynatrace

Dynatrace Software Intelligence Platform is a network, application, and infrastructure monitoring platform that includes AIOps, digital experience management, and digital business analytics.

Users can observe how they are performing and instantly identify microservices that are performing poorly since the platform automatically detects and monitors microservices running within containers.

With the help of PurePath Technology from Dynatrace, timing, and code-level context are automatically recorded for all transactions at every tier. This makes Dynatrace a top choice among observability tools for complete monitoring and analysis

#9 LogicMonitor

LogicMonitor offers SaaS-based data center monitoring of physical, virtual, and cloud-based IT infrastructures. Performance monitoring, historical trending, reporting, and email/SMS alerting are all features of the software that help IT employees be forewarned of potential problems before they can disrupt operations.

From a single web-based panel, LogicMonitor offers a pre-configured, out-of-the-box monitoring tool for most suppliers of switches, routers, firewalls, load balancers, servers, applications, databases, VoIP systems, and storage.

#10 Monitis

Monitis was established in 2006 to offer a comprehensive cloud-based observability monitoring solution to company executives, IT professionals, and service providers.

Since Monitis is a cloud-based solution, very little software needs to be downloaded to use most of its features. Although some services necessitate the installation of an agent or the integration of certain code into the website, etc., they normally involve little work and can be finished quickly.

#11 Nagios

ccan support system, application, service, and business process monitoring observability tools in DevOps environment. It offers capabilities for tracking all types of applications, including Windows, Linux, UNIX, and Web apps, and their current states.

This tool runs tests quickly and can be set up easily on both the client and server sides. It monitors the network's switches, routers, and other hardware to look for network overload and other issues. Nagios enables flexibility to monitor your servers using agent-based and agentless monitoring, with over 5000 different add-ons readily available.

#12 New Relic

New Relic is a cloud-based monitoring tool that offers full-stack observability in a single secure cloud. It supports applications created in Ruby, Java, .Net, PHP, and Python.

Due to its pay-as-you-go Mechanism, teams can correlate a complete stack to visualize and debug issues more quickly, and they only pay for the resources they really utilize.

A live and detailed view of your network, infrastructure, applications, end-user experience, machine-learning models, and more is provided via full-stack monitoring. When a model gets less accurate, warnings and alarms from New Relic Applied Intelligence help to increase trust.

#13 Pingdom

Pingdom is a service that tracks a website's uptime, downtime, and performance. It monitors websites from various locations worldwide to distinguish between true downtime and access and routing issues.Pingdom uses servers spread across several nations to gauge the websites it monitors' response times. It can indicate whether a website is down due to network breaks or DNS server failure.

Pingdom works by often visiting websites to determine whether they are user-accessible. Once the software deems that the website is once again operational, it will continue to monitor it more frequently. Pingdom is widely recognized as one of the top observability tools for ensuring website reliability and performance.

#14 Sumo Logic

Sumo Logic's Application Observability tool offers visibility into performance metrics, logs, and events, as well as distributed tracing, and is a highly scalable, secure, and economical analytics platform. It provides automated finding of new infrastructure and services in pre-made dashboards.

Application issues can be diagnosed more quickly by displaying the service dependencies in service maps. Real user monitoring records every click made within the application and rapidly identifies underperforming pages. Use the Root Cause Explorer, which is powered by ML, to automate anomaly detection.

#15 UptimeRobot

UptimeRobot is an online uptime monitoring service that monitors website uptime. You can ping your server, examine the content of a website, check up on up to 50 URLs, and check your ports every five minutes. To demonstrate your uptime, you can develop a status page.

You can use the Heartbeat monitor to keep an eye on your cron processes or monitor SSL certificates. You can adjust and control your monitoring with maintenance windows or advanced notifications to avoid receiving alerts during maintenance or to start receiving notifications after a specific amount of downtime. UptimeRobot serves as a reliable observability tool for ensuring the continuous availability and performance of your online services.

#16 Airbrake

Airbrake is a lightweight APM for error and performance monitoring tool. It enables developers to track the performance and availability of their applications.

It is used by thousands of engineering teams around the globe to record, group, and notify software exceptions, which enables them to identify and address defects more quickly. Every month, Airbrake processes over 600 million errors for organizations including Zendesk, Groupon, TED, Soundcloud, and countless others.

#17 Better Uptime

Better Uptime is a vastly improved infrastructure monitoring and observability tool that notifies the appropriate team member when something goes wrong. Schedule on-call responsibilities, get useful notifications, and work together more quickly than ever to resolve incidents.

Get a lovely branded status page for your domain so you can educate your users. It is made with more than 100 integrations to fit into your workflow.

#18 Rollbar

Rollbar is a bug-tracking and monitoring tool available in the cloud for businesses of all sizes. It supports numerous programming languages and frameworks, including JavaScript, Python, .NET, Drupal, WordPress, and Pyramid.

Rollbar offers customers the flexibility to modify these grouping rules and provides automated error grouping depending on an issue's primary cause. Users can set and order notifications using notification filters according to the type of error. Additionally, the system has workflow control features that let users mark concerns as active, resolved, or muted.

#19 Logmatic.io

Logmatic.io is a SaaS log management tool that enables customers to monitor and debug applications more effectively and quickly. It is also a platform for operations data, including machine and log events. By utilizing machine data, we assist businesses in enhancing the effectiveness of their software and operations.

You can get a clear overview of what's happening throughout your distributed environments, and you'll quickly find the proverbial needle in the haystack. Logmatic.io serves as a powerful observability tool for gaining insights into distributed environments and optimizing software performance and user experience.

Create dynamic assessments and pinpoint improvements for your business, your software, and the user experience.

#20 Sentry

Sentry offers real-time error tracking for your web, mobile, and gaming applications, giving developers the knowledge they need to identify and fix errors. Sentry was developed at DISQUS as an open-source observability platform project to address the issue of exception logging in a Django application.

Since then, it has expanded to support a wide range of well-known programming languages and operating systems, including Python, PHP, Java, Ruby, Node.js, and JavaScript.

#21 Grafana

Grafana, a well-known open-source observability platform for analytical and visualization web layers, now offers a comprehensive observability stack with Grafana Cloud. Their tools, including Tempo for tracing, Prometheus for metrics, and Loki for logs, make the offering an exceptional value for someone just getting started with observability.

Additionally, they provide self-managed versions of their technologies, which has become the norm. This is quite advantageous due to the extensive ecosystem support and accessibility of ready-made dashboards for a range of tools and use cases.

#22 Instana

Instana is an application performance monitoring tool that manages services, infrastructure, containers, websites, and fully automated APM. The tool offers automatic distributed tracking for full visibility as well as a continuous full-stack observability tool of all server and application components.

For analysis, visualization, and alerting via UI customizations, Instana offers application perspectives that logically group app components, services, and transaction requests together.

#23 AppNeta

AppNeta Performance Manager is a network and application performance monitoring tool that provides visibility into business-critical networks and apps as experienced from remote locations. The platform utilizes a four-dimensional monitoring system that looks at web applications, network flows, network packets, and network pathways.

As part of its monitoring solution, AppNeta's APM technology includes 2,000 built-in applications and gives users the option to create their own custom application definitions as necessary.

#24 Epsagon

Epsagon is a SaaS platform that gives Dev and Ops teams automated data correlation, payloads, and end-to-end observability tooling in microservice settings. This reduces the amount of time spent on troubleshooting and allows issues to be resolved more quickly.

Epsagon offers automated instrumentation and tracing without coverage gaps with our small-footprint agent SDK, providing you complete visibility for containers, VMs, serverless, and more with no need for manual coding, tagging, or maintenance.

Epsagon assists businesses and teams of all sizes in reducing expenses with unlimited free monitoring and scalable pricing, as well as in boosting engineering effectiveness through decreased application downtime, quicker feature shipping, and time saved in issue identification and resolution.

#25 Prometheus

Prometheus is a well-liked open-source toolkit for system monitoring and alerting. It was created especially to monitor contemporary applications. It allows for monitoring Linux servers and Kubernetes, and it records metrics as time series data.

Additionally, we can enable short-lived jobs by using push gateway and specialized exporters like HAProxy, StatsD, Graphite, etc. Prometheus is designed to operate on a single server node and does not rely on distributed storage.

#26 Honeycomb

Honeycomb is an observability tool created for DevOps teams to monitor, troubleshoot, and enhance live production software. Users can see codes being issued proactively due to its user-friendly UI/UX.

It offers quick feedback loops for the dependable distribution of effective features. It establishes, evaluates, and modifies engineering priorities by Service Level Objectives (SLOs). Distributed tracing can easily be used to delve into traces, queries, or visualizations.

#27 Sematext

Sematext provides Site Search Analytics, Logsene Log Management & Analytics, SPM Performance Monitoring, and other search-related products. It integrates them all into a single observability tool to aid businesses in quicker problem-solving.

To investigate and notify companies, it employs pre-defined or personalized dashboards. Monitoring infrastructure, database, application, and site response times offers quicker root cause analysis. Both metrics and logs can have anomaly detection and alarms set up. You can quickly set up your account due to integrations with lightweight data shippers like Sematext, Kubernetes, and Docker.

#28 Icinga

Icinga is an open-source network observability tool resource monitoring tool that checks for network resource availability, alerts users to outages, and produces useful data for performance reporting. With its quick and well-organized web interface and five Icinga status colors, issues can be quickly identified.

By grouping and filtering components, you can create unique views. Keep them in the dashboards. Icinga offers visualization options for a wide variety of use scenarios and provides detailed views, operational procedures, and certificate surveillance. Its built-in clustering mechanism provides scalability, automation, and extensive configuration options.

#29 Zabbix

Zabbix is an open-source solution observability platform that monitors networks, servers, virtual machines, and cloud services may all be monitored using. You can use Zabbix for much more than just monitoring, and no additional fees are involved.  You can also offer monitoring services to different customers in multi-tenant environments.

It collects desired data from any source at chosen intervals. It uses values from a backend database to build triggers, which are configurable problem thresholds. According to the receiver, media type, and escalation timetable, it customizes sending notifications.

#30 Checkmk

Checkmk is another open-source monitoring and observability tool with many capabilities for businesses of all sizes. It makes it simple to get started and pool monitoring data from your stack because it supports a large number of packages and plugins. It provides monitoring for both traditional IT infrastructure and IT assets built for the cloud.

This is the tool of choice if you need to monitor IoT devices or network requests. They have packages for Docker, Virtual Appliance, and all popular Linux systems.

#31 Netdata

Netdata is an open-source observability tool for gathering real-time metrics on CPU usage, disk activity, bandwidth usage, website visits, etc., and then showing them in simple-to-understand real-time charts.

The tool is made to show activity in as much detail as possible, giving the user a clear picture of what is occurring and what has just occurred in their system or application.

It automatically gathers more than 5,000 metrics, requires no configuration, dependencies, or upkeep, and includes more than 100 pre-configured alarms to find common failures, performance, and availability problems.

#32 Observium

Observium is a Network Observation and Monitoring System (NOMS) that gathers data from devices using SNMP and displays it via a web interface. It is heavily reliant on the RRDtool package and is built on the PHP programming language and MySQL database. Minimum involvement, maximum automation, and maximum information accessibility are among Observium's simple basic design objectives.

#33 PRTG Network Monitor

Paessler Router Traffic Grapher is a server uptime and utilization tool, network observability tool, and bandwidth usage software package for server infrastructure by Paessler AG. Using SNMP, Packet Sniffing, and Netflow, it can track and categorize bandwidth utilization within a network. It supports both Linux and Microsoft Windows.

Finally!!!

Using the correct observability tools would directly benefit key business metrics and lessen downtime inconvenience. Many of the above tools have free plans and offer fantastic value right out of the box.

Although there is a continuing trend to add more observability tools to the stack, your decision and requirement for those tools would depend on an analysis of your current stack, issues, and technologies used. The number of providers you select will depend on your scale and budget.

We hope this article gave you knowledge of fresh tools and suggestions to help you select your tool.

Monitor Your Entire Application with Atatus

Atatus is a Full Stack Observability Platform that lets you review problems as if they happened in your application. Instead of guessing why errors happen or asking users for screenshots and log dumps, Atatus lets you replay the session to quickly understand what went wrong.

We offer Application Performance Monitoring, Real User Monitoring, Serverless Monitoring, Logs Monitoring, Synthetic Monitoring, Uptime Monitoring, and API Analytics. It works perfectly with any application, regardless of framework, and has plugins.

Atatus can benefit your business, providing comprehensive view of your application, including how it works, where performance bottlenecks exist, which users are most impacted, and which errors break your code for your frontend, backend, and infrastructure.

If you are not yet an Atatus customer, you can sign up for a 14-day free trial.

New Relic and AppDynamics are leading monitoring and observability tools for monitoring the performance of your applications and network. Both help you keep track of how well your systems are running, but they do it in different ways.

New Relic is a great tool for APM, making it versatile for tracking overall system performance. It also features an easy-to-use interface and strong data visualization.

AppDynamics is great for detailed problem analysis and monitoring complex systems, making it suitable for large organizations. In this blog, we will compare New Relic and AppDynamics to show their strengths and help you decide which tool fits your needs best.

In this blog post:

1. What is New Relic?
2. What is AppDynamics?
3. Application Performance Monitoring(APM)
4. Log Management
5. Infrastructure Monitoring
6. Synthetic Monitoring
7. Documentation
8. Pricing Comparison
9. Final Verdict: New Relic vs AppDynamics
10. Atatus: A Better New Relic and AppDynamics Alternative

What is New Relic?

New Relic is a monitoring tool that offers engineers real-time insights into the performance and behaviour of digital systems. With over 30 features and support for more than 750 integrations, it leverages AI-driven insights to help users troubleshoot issues and enhance performance.

The platform provides a comprehensive set of tools, including Application Performance Monitoring (APM), log monitoring, infrastructure monitoring, and real-user monitoring. As a result, New Relic delivers a holistic view of your system, enabling more effective monitoring, improved reliability, and increased efficiency.

Key Features of New Relic

• Full-Stack Observability: New Relic offers comprehensive visibility across your entire system, covering applications, infrastructure, and logs, all within a single platform.
• Real-Time Monitoring: It provides real-time monitoring and alerts, allowing you to detect and address performance problems as they occur.
• AI-Powered Insights: With AI and machine learning, New Relic automatically spots anomalies and delivers insights to help you quickly pinpoint and fix issues.
• SIEM: New Relic's SIEM feature integrates security information and event management to provide real-time visibility into application security and performance.
• Custom Dashboards: Users can build customizable dashboards using drag-and-drop widgets, making it easy to track key metrics and performance data in a way that suits their needs.
• Distributed Tracing: This feature lets you follow requests as they move through services, offering a clear view of complex microservices systems.
• Integrations: New Relic integrates with many popular tools and platforms, ensuring smooth data flow and boosting your monitoring capabilities.

What is AppDynamics?

AppDynamics is a comprehensive business observability platform that provides deep insights into the performance of your entire technology stack. It offers tools for monitoring infrastructure, applications, databases, and SAP environments, allowing you to track performance metrics in real time.

The platform includes Application Performance Monitoring (APM) to identify and resolve performance issues, End-user Monitoring to track user interactions, and AI-powered insights to help quickly pinpoint problems. With its robust monitoring capabilities, AppDynamics enhances both operational efficiency and user experience.

Key Features of AppDynamics

• Cloud Migration: Plan and manage cloud migrations smoothly with full visibility into applications, infrastructure, and network functions.
• Infrastructure Monitoring: Monitor networks, servers, and containers to ensure infrastructure stability and performance.
• Cloud Native Application Observability: Correlate all telemetry data for a unified view across the entire cloud native environment.
• SAP Monitoring: Monitor the entire SAP environment for seamless performance across the SAP landscape.
• Flow Maps: Visualize application flows with tiers, nodes, and business transactions in one clear, dynamic map.
• Microservices: Monitor business transactions and health across microservices and containers (AWS, Docker, Kubernetes) for efficiency.
• Serverless Monitoring: Ensure seamless user experiences by tracking serverless functions and gaining end-to-end visibility of performance.

Application Performance Monitoring(APM)

New Relic

Signing up for a New Relic account was quick and easy. After that, I installed the language agent for my application. These agents automatically collect performance metrics from the app and send them to New Relic APM.

Once the agent was installed, I generated some traffic to the application and logged into my account. Within a few minutes, data started flowing, and I could see pre-built dashboards on the APM Summary page. These dashboards provided an instant overview of my application's performance without needing any customization.

AppDynamics

Using AppDynamics has been very helpful for monitoring both application and business performance. It provides real-time insights and automatically detects issues to keep things running smoothly.

The tool automatically gathers data and metrics, creating clear flow maps that show how data moves between different components, like web services, message queues, and databases. For example, in an e-commerce app, you can see exactly how data flows through various parts of the system.

The dashboard then aggregates key metrics, such as load, response time, and errors, making it easier to monitor performance and fix problems quickly.

New Relic vs AppDynamics for APM: Both New Relic and AppDynamics offer excellent APM capabilities, each with distinct strengths that cater to different needs and preferences.

Log Management

New Relic

Once I set up New Relic, I noticed that it automatically begins collecting logs from applications and displays them under the logs tab. It seamlessly captures both infrastructure and application logs without requiring any additional configuration. You can also forward logs to New Relic using various options, like the APM agent, infrastructure agent, third-party log services, the Log API or TCP endpoint, and even the OpenTelemetry SDK.

New Relic logs offer a deeper level of visibility into performance data, whether it’s events, errors, or traces. This has been incredibly useful in reducing MTTR (mean time to resolution) and addressing production issues quickly. The logs UI consolidates everything into one place, making it easy to spot key patterns.

I also appreciate the ability to explore more context around individual log lines, which provides even better insights into what’s happening within the application and infrastructure.

AppDynamics

To begin with log collection, you will need to set up a log collection agent and a log analytics engine. The agent handles gathering logs from your Kubernetes or App Service environment. AppDynamics offers flexibility by supporting a range of log collection agents like Fluentd, Logstash, and Filebeat.

Once your logs are collected, you can dive right into AppDynamics, where you will have the ability to search, filter, and analyze the data in real-time.

One feature I find particularly useful is the custom log parsing rules. These allow you to extract specific fields from the logs, making it much easier to generate custom metrics and build tailored dashboards. This has really helped me track key application metrics and spot trends over time. The real-time insights are invaluable, especially when you need to stay on top of performance issues.

New Relic vs AppDynamics for log management: AppDynamics offers better log management capabilities than New Relic.

Infrastructure Monitoring

New Relic

To get started with infrastructure monitoring, the first step is installing the agent, which can be done on Linux, macOS, or Windows systems. New Relic provides various deployment options based on your observability needs, and the documentation is clear, making the installation process smooth.

New Relic's infrastructure monitoring has given me excellent visibility into system performance. The Inventory page is great for quickly searching through all infrastructure entities across different hosts, while the Events page allows me to easily track configuration changes, restarts, SSH sessions, and more. The data is collected and displayed securely, ensuring my monitoring stays accurate and up-to-date.

AppDynamics

Installing application agents with the Controller UI is simple, especially with the getting started wizard. Setting up service monitoring through the service availability section in AppDynamics home is also quick and straightforward.

I rely on the metric browser to track infrastructure metrics, making it easy to manage both traditional and cloud environments. AppDynamics helps connect performance data with business goals, and its automated data collection makes monitoring effortless. For Kubernetes and containerized apps, the observability features are extremely helpful for quickly identifying and fixing performance issues.

New Relic vs AppDynamics for infrastructure monitoring: When it comes to infrastructure monitoring, AppDynamics excels with its in-depth insights and robust features.

Synthetic Monitoring

New Relic

Once I signed into my New Relic account, I went straight to one.newrelic.com and navigated to synthetic monitoring. From there, I clicked on Create a monitor and chose the Page load performance option. I then pasted the URL of the page I wanted to test in the URL field.

Once the monitor started reporting, I could easily see all the data on the Synthetics Summary page. It gave me a clear overview of my website's performance, including trends in request/response times, connection times, and any errors. For detailed results, I headed to the Results page, where I could review individual monitor data. The Page Load Time graph was especially useful, giving me a quick snapshot of how performance changed over time.

When it came to troubleshooting, the Failures page helped me pinpoint downtime incidents and errors. I could select individual incidents to view them in detail and understand what went wrong.

AppDynamics

After opening my browser application and navigating to the Jobs page, I created a new job by clicking Create a New Job or Add for existing ones. On the Synthetic Job Dashboard, I monitored availability and performance trends. The Performance Trend column showed the performance status for each job.

In the Sessions page, I sorted session results and examined specific details. The Charts tab offered useful widgets, like the Lowest Availabilitys widget, which helped me identify and resolve location-specific issues efficiently. This setup provided a clear and effective way to monitor job performance.

New Relic vs AppDynamics for synthetic monitoring: AppDynamics does synthetic monitoring better than New Relic because it collects data from both real users and synthetic users.

Documentation

New Relic

New Relic’s documentation stands out for its well-structured format, offering clear step-by-step explanations that make it easy to follow, even for beginners. The inclusion of examples and visuals further enhances the learning process, allowing for quicker understanding. Overall, it feels intuitive and user-friendly, which helps streamline the setup process and saves time.

AppDynamics

AppDynamics' documentation is informative but not as well-organized, making it hard to find specific information, which can be frustrating. While the content is helpful, it often needs more explanation to be fully clear. The navigation between topics isn’t smooth, so locating the right sections takes more time.

New Relic's documentation is well-structured and user-friendly, making it easy for beginners to understand and follow.

Pricing Comparison

New Relic

New Relic offers a free plan and several paid options based on how much you use. It gives you 100GB of free data each month, and if you go over that, it costs either $0.30 or $0.50 per GB, depending on your plan. There are four pricing levels, including a plan that's free forever.

AppDynamics

AppDynamics uses a subscription based pricing model, where businesses pay a set fee every month or year to access the platform's features, no matter how much they use. This makes budgeting easier but can lead to unused resources and extra costs.

For a more budget-friendly option, go with New Relic.

Final Verdict: New Relic vs AppDynamics

• New Relic is ideal for small to mid-sized businesses, while AppDynamics is best for large enterprises.
• New Relic offers a simpler, more intuitive interface, whereas AppDynamics has a steeper learning curve but provides more depth.
• AppDynamics offers more advanced monitoring features, while New Relic provides essential tools sufficient for businesses.
• AppDynamics allows for greater customization, whereas New Relic is easier to set up and use out of the box.
• New Relic generally has a lower starting price, though AppDynamics may require a higher investment but can deliver more value for larger teams.
• AppDynamics scales better for large applications, while New Relic is suitable for businesses that are growing.

Atatus: A Better New Relic and AppDynamics Alternative

New Relic and AppDynamics have their drawbacks even as a powerful monitoring and observability tools, often due to their complexity and steep learning curves.

In such cases, you may need an alternative that offers a simpler, more user-friendly interface for effective Application Performance Monitoring (APM) and observability.

Atatus stands out as a better alternative to New Relic and AppDynamics, offering a full-stack Application Performance Monitoring (APM) and observability solution. It is known for its simple, user-friendly interface, unlike the more complicated setups of New Relic and AppDynamics.

Atatus, being a native OpenTelemetry tool, integrates metrics, logs, and traces giving you a complete view of your systems. Its built-in distributed tracing feature provides end-to-end visibility of services, ensuring a unified and thorough approach to observability that goes beyond what tools like New Relic and AppDynamics offer.

Atatus is also valued for its affordability and clear pricing. With Atatus, there are no hidden costs. What you see is what you pay, making it a budget-friendly option compared to New Relic and AppDynamics.

Curious to see how Atatus can improve your monitoring? Sign up today and discover how easy and affordable observability can be!

Grafana is powerful, but power without simplicity is just complexity. If your team is drowning in YAML configs, fighting data source plugins, or struggling to give non-technical stakeholders a clean view of what's happening in production, you're not alone.

Grafana is a popular open-source data visualization platform. With its frontend written in TypeScript and a Go back-end, it allows users to create interactive dashboards using data from sources like InfluxDB, Prometheus, and Elasticsearch. But it uses a time-series database as its default, making it great for IT metrics like server uptime and API latency and far less suitable for business metrics that require segmentation by customer type, product category, or geography.

And for non-technical users? Its extra-technical nature is consistently cited as a major frustration. That's why thousands of engineering teams are evaluating alternatives.

The good news: there's a rich ecosystem of Grafana alternatives, each solving specific pain points. This guide cuts through the noise to help you find the right one.

In This Blog:

• Why Teams Are Moving Away from Grafana?
• What to Look for in a Grafana Alternative?
• Quick Comparison Table
• The 10 Best Grafana Alternatives (Detailed Breakdown)
• Final Recommendation

Why Teams Are Moving Away from Grafana?

Grafana is mature and capable, but it comes with real trade-offs that push teams to look elsewhere. Here are the most common pain points we hear:

What to Look for in a Grafana Alternative?

Before evaluating tools, anchor your decision to what your team actually needs. Here are the criteria that matter most:

• Full-stack observability: Metrics, logs, traces, and frontend performance — ideally in a single platform without data silos.
• Ease of setup: Time-to-value matters. Look for auto-instrumentation and pre-built dashboards that work out of the box.
• Business-friendly UX: Your VP of Engineering shouldn't need a tutorial to read a dashboard. Prioritize clean, intuitive interfaces.
• Alerting that works: Smart, threshold-based, and anomaly-detection alerts — with minimal noise and easy routing to Slack, PagerDuty, etc.
• Transparent pricing: Avoid tools that hide costs behind data ingestion fees. Look for predictable, per-host or per-seat pricing.
• Real User Monitoring (RUM): Understanding how actual users experience your app is as important as backend metrics.
• Support quality: When production is down, you need a real human — not a community forum.

Quick Comparison Table

Use this table to quickly narrow down your options before diving into the full tool breakdowns below.

The 10 Best Grafana Alternatives (Detailed Breakdown)

1. Atatus
2. Kibana
3. Instana
4. Datadog
5. New Relic
6. InfluxDB
7. Dynatrace
8. Prometheus
9. Cyclotron
10. Redash

1. Atatus

Atatus is a SaaS-delivered application performance and error-tracking tool that offers full-stack visibility for all your applications.

Millions of performance data points from across your applications can be dynamically collected by the platform, enabling you to resolve issues more rapidly and enhance the digital user experience.

And with the flexibility of cloud or on-premise deployment, everything takes place in real-time in the field.

Atatus provides a user-friendly interface for creating custom dashboards to display metrics, alerts, and other data, making it easy for organizations to display data in a way that makes sense for their business needs.

It supports alerting based on performance thresholds, making it easy to monitor performance and receive notifications when performance issues arise.

Pros:

• User-friendly interface.
• Real-time monitoring of performance data.
• Tools for exploring and analyzing data.
• Interactive model.
• Wide range of performance metrics such as response time, latency, throughput, apdex score, HTTP failure codes and much more are covered.
• Regular, precise alerts feature.

2. Kibana

Kibana is a web-based analytics platform that collects data from different sources and displays them. It is a part of the three-pronged Elasticstack along with Logstash and Elasticsearch.

Since it's written in java, developers and non-developers can work on it alike.

Using Kibana with Elasticsearch, you can search, view, and visualize indexed data. It allows you to create charts, tables, histograms, and maps. Multiple visualization methods are combined in its Dashboards to provide a processable view of large data sets.

Kibana performs well when visualizing data from Log management, Infrastructure monitoring, APM, SIEM, or Business.

Kibana’s data visualization pointers:

• Kibana offers various types of visualizations, such as bar charts, pie charts, line graphs, etc. Choose the visualization that best represents your data.
• You can filter the data based on specific conditions to show only the relevant information.
• It provides various aggregation features, such as sum, average, count, etc., which can be applied to the data to summarize it and make it easier to understand.
• Kibana offers various options to customize the visualizations, such as changing the axis labels, adding a title, adjusting the colors, etc.
• You can save visualizations and share them with others by exporting them or embedding them in a dashboard.
• It also allows you to create multiple visualizations and combine them in a dashboard to understand your data better.
• Kibana has a rich library of plugins that can enhance its capabilities, such as adding new visualizations, integrating with other tools, etc.

Among Kibana's main advantages is that it allows you to analyze logs in a way that Grafana cannot.

Cons:

• It is an integrated product; it requires services from other products of the ELK stack
• Less number of dashboards

3. Instana

Instana is a cloud-based observability platform for application performance management (APM) and IT operations.

It helps organizations to monitor and manage their microservice-based applications and infrastructure by providing real-time visibility, automated anomaly detection, and performance optimization.

Instana uses artificial intelligence and machine learning algorithms to automatically detect performance issues, identify root causes, and provide recommendations for resolution.

• Customizable dashboards provide a consolidated view of key performance metrics, including CPU utilization, memory usage, request latency, and more.
• Interactive topology maps provide a visual representation of the components of an application and their relationships, helping teams to understand how changes in one component can impact other parts of the application.
• Graphs and charts that display performance metrics over time
• Instana provides detailed traces of individual requests, showing the flow of requests through the different components of an application and highlighting areas of potential performance bottlenecks.
• Customizable alerts can be set up to trigger notifications when performance thresholds are breached

Cons:

• No support for profiling or real user monitoring

4. Datadog

Datadog is a cloud-based monitoring and analytics platform that helps organizations monitor and optimize their applications, infrastructure, and business processes.

The data visualization process in Datadog provides teams with the tools they need to effectively monitor and understand the performance of their applications and infrastructure, enabling them to identify and resolve issues and improve performance quickly.

• Datadog integrates with a wide range of systems and applications to collect performance data, including cloud services, infrastructure, logs, and custom metrics.
• It provides a range of tools for analyzing performance data, including interactive dashboards, alerting, anomaly detection, and correlation with logs.
• The visualization features include time series graphs, heat maps, histograms, and pie charts.
• Teams can customize visualizations to fit their specific needs by adjusting the data displayed, changing colors, adding annotations, and more.
• Datadog provides options for sharing visualizations with others, including embedding in other tools, exporting, and sharing links.
• It also provides collaboration features, such as the ability to comment on visualizations, to enable teams to work together and make data-driven decisions.

Cons:

• Very complicated UI
• No public playground
• Features mismatch and tend to overlap

5. New Relic

New Relic is a software analytics platform that provides comprehensive monitoring, troubleshooting, and data visualization capabilities for modern software applications and infrastructure.

New Relic is a leading observability platform based on distributed tracing. It provides the following functions:

• Real-time monitoring
• Trace analysis in multi-tier applications
• Customizable bar charts, line graphs, and more
• They also provide visibility into mobile application performance

Cons:

• Complicated user interface
• On the expensive side

6. InfluxDB

InfluxDB is a real-time analytics data store that manages metrics, events, and real-time analytics. InfluxDB does not replace Grafana, but you can use it as a Grafana data source.

It is a time series database that visualizes data through its visualization tool, InfluxDB Chronograf.

• InfluxDB Chronograf provides a user-friendly interface for creating custom dashboards to display metrics, alerts, and other data.
• A range of pre-built templates
• Tools for exploring and analyzing data, including a query builder and the ability to interact with data in real time.
• Designed specifically for time series data, InfluxDB Chronograf provides a range of time series visualizations, including line graphs, bar charts, and more.
• Easy integrations to consolidate data from multiple sources in a single view.

Cons:

• Must be used with grafana or other alternatives
• Have to learn InfluxQL
• Slower than other time series database

7. Dynatrace

Dynatrace is a software intelligence platform that provides comprehensive monitoring and data visualization capabilities for digital business applications and cloud infrastructure.

• Customizable dashboards with metrics graphs
• Interactive topology maps
• Dynatrace provides detailed traces of individual requests, showing the flow of requests through the different components of an application
• Dynatrace uses artificial intelligence and machine learning algorithms to automatically identify the root cause of performance issues, even in complex, multi-tier applications.
• It also provides real-time performance data, enabling teams to identify and resolve issues in near real-time quickly.

Cons:

• Problems of false alarm
• Unstable pricing model

8. Prometheus

Prometheus is a popular open-source monitoring and alerting system that provides data visualization capabilities. It records metrics in a time series database using an HTTP pull model.

Prometheus provides a web-based graphical user interface (GUI) for creating custom dashboards to display metrics, alerts, and other data.

• Prometheus provides PromQL for customizing dashboards
• It highly supports histograms, making the interpretation of data simpler
• Labels are key-value pairs that can be used to categorize and filter metrics, making it easy to compare metrics across different systems, applications, or other dimensions.
• Alerting features based on metrics threshold

Cons:

• Limited visualization available
• Scaling not feasible

9. Cyclotron

Cyclotron provides a range of interactive visualizations, including bar charts, line graphs, heat maps, and more, enabling organizations to display data in a way that makes it easy to understand and analyze.

With Cyclotron, you can retrieve data from numerous data sources, including Elasticsearch, Graphite, InfluxDB, Prometheus, and others.

• Drag-and-drop interface for creating dashboards and reports
• Cyclotron provides a range of data connectors, making it easy to connect to various data sources, including databases, cloud services, and APIs.
• Provides tools for filtering and transforming data
• Allows embedding, exporting, and sharing links

Cons:

• Not interactive
• Lesser pre-built dashboards

10. Redash

Redash is yet another popular data visualization platform. With drag and drop, easy navigation, and resizable visualization features, this tool can be handy. Also, sharing dashboards with your teammates is a breeze.

Its other useful features include:

• Querying in natural syntax
• More than 35 SQL and NoSQL data sources.
• Keyboard shortcuts and live autocompletion
• Cache management
• Custom snippets for frequently used codes.
• Integrations with popular databases and platforms

Cons:

• Complex SQL queries

Final Recommendation

Choosing a Grafana alternative comes down to understanding what Grafana is actually missing for your team. Here's a quick decision framework:

For most engineering teams, especially those moving away from Grafana because of complexity, cost, or the pain of maintaining multiple monitoring tools, Atatus is the clear recommendation. It delivers full-stack observability out of the box, with a pricing model that doesn't punish growth and a UI your whole team can actually use.

As always: keep your requirements in mind. The best tool isn't the most expensive or the most feature-rich, it's the one that solves your specific problems without creating new ones.

New Relic and Splunk are two prominent tools in the world of observability and monitoring, each serving distinct purposes. New Relic is used for Application Performance Monitoring (APM), offering a full-stack observability platform . It is important to note that New Relic is not a  SIEM tool, its primary focus is performance monitoring.

On the other hand, Splunk is used for log management, machine data analytics, and is widely utilized as a SIEM  tool. By comparing these tools, organizations can determine which solution best fits their needs.

Let’s get into it!

In this blog:

1. What are New Relic and Splunk?
2. Application Performance Monitoring(APM): New Relic
3. Log Management: Splunk
4. Alerts and Notifications
5. OpenTelemetry Support
6. Machine Learning Capabilities
7. UI and UX
8. Documentation
9. Pricing
10. New Relic vs. Splunk: Key Takeaways
11. An alternative to New Relic vs Splunk : Atatus

What are New Relic and Splunk?

Let’s start with a quick overview,

New Relic:

New Relic provides a cloud-based observability platform designed to deliver real-time insights into both applications and infrastructure. New Relic is widely regarded as a good tool, especially for Application Performance Monitoring (APM). It allows for comprehensive performance monitoring by evaluating metrics, logs, and traces from diverse sources. Its AI-enhanced capabilities improve system reliability and offer a holistic view of your digital environment.

Splunk:

Splunk is a powerful platform designed for analyzing and visualizing machine-generated data. It excels at managing large volumes of logs and metrics, offering advanced analytics and security insights. Splunk efficiently collects, organizes, and indexes real-time data, and provides detailed alerts, dashboards, and reports. It is available in both cloud and enterprise versions.

Application Performance Monitoring(APM)

New Relic APM:

I signed up for a New Relic account and installed the language agent for my app. After that, I just needed to generate some traffic to the application and log in to my New Relic account. Within a few minutes, I started seeing data flow into the platform.

Once New Relic received data from my app, I could monitor its basic health right away. The APM Summary page in the New Relic UI provided basic dashboards that offered immediate insights into my app's performance, with no need for customization.

I used the APM Group Errors tab to dynamically filter and group errors. And the Error profiles helped me see which users were most common across all error events during the selected time window.

I then clicked on Attribute Profiles to expand into individual profiles.

I also utilized distributed tracing to visualize the path of any service request, which really helped in understanding and troubleshooting issues.

Splunk APM:

I set up Splunk APM by installing the SignalFx-SmartAgent on my server. If that wasn’t an option, I could use the OpenTelemetry Collector instead. Then, I updated the configuration file to accept APM traces. Finally, I installed and initialised SFx tracing library to my app so it could start sending data to Splunk.

In the Splunk APM, I clicked the APM tab to navigate to the service map. This provided instant visibility into service interactions, inferred services, dependencies, and performance. I could see all instrumented services.

Clicking the Troubleshooting tab showed service latency and root errors.

I selected a service from the service map and chose “View Dashboard” to access service and system metrics.

To view and drill down into a trace, I clicked the “Show Traces” button at the bottom left of the troubleshooting service map. I found options to filter traces by environment, service, tags, and timeframe. After applying these filters, I clicked on individual Trace IDs and spans to view their detailed metadata.

New Relic vs Splunk for APM: New Relic is a better choice with its quick setup and deeper insights. It offers strong monitoring features with minimal effort.

Log Management

New Relic:

Once I set up New Relic, it automatically started collecting logs from my applications and displaying them in the logs tab. It captures both infrastructure and application logs seamlessly without any extra setup needed.

New Relic logs gave me deeper visibility into my application and infrastructure performance, showing critical data like events, errors, and traces. This made it easier to reduce MTTR and quickly troubleshoot production issues.

I could visualize everything in one place using the logs UI, which helped me identify important patterns. Plus, I could dive deeper into specific log lines to gain more context and better insights.

Splunk:

Splunk is known for its strong log management and can manage large amounts of data, but it doesn’t automatically gather logs from applications, it requires manual setup.

To get started with Log Observer Connect, you can connect with either Splunk Enterprise or Splunk Cloud Platform using two different methods.

With Log Observer Connect, It it easy to troubleshoot application and infrastructure behaviour using high-context logs. You can perform codeless queries on your Splunk Enterprise or Splunk Cloud Platform logs to quickly detect the source of issues, and then jump to related content throughout Splunk Observability Cloud with just one click.

Here’s what you can do using Splunk Log Observer Connect:

• View overall system health using the timeline.
• Query logs in Log Observer.
• Browse logs in the logs table.
• Search logs by keywords or fields.
• Group logs by fields using log aggregation.
• Save and share Log Observer queries.

New Relic vs Splunk for Log Management: While New Relic is easier to set up, Splunk offers more in-depth control and insights.

Machine Learning Capabilities

New Relic

1. Integrated ML Algorithms: Automates performance monitoring and troubleshooting tasks.
2. Anomaly Detection: Identifies performance issues and outliers.
3. Insights and Recommendations: Provides optimization tips for applications and services.

Splunk

1. Machine Learning Toolkit (MLTK): Includes pre-built algorithms for common use cases.
2. Extensive Framework Integrations: Works with popular ML frameworks.
3. Versatile Use Cases: Handles data classification, anomaly detection, and predictive analytics.

New Relic vs Splunk: Splunk excels with its advanced machine-learning features for complex needs.

OpenTelemetry Support

New Relic offers OpenTelemetry integration, but may have limitations in advanced features. It provides basic support, allowing you to collect and visualize telemetry data, but full optimization is still in progress.

Splunk also supports OpenTelemetry, but like New Relic, its integration might not be fully optimized. Splunk allows for data collection and monitoring, but you might face some gaps in advanced functionalities and ease of use.

New Relic vs Splunk for OpenTelemetry Support: Tie, Both New Relic and Splunk offer OpenTelemetry integration but may have limitations in advanced features. For optimal OpenTelemetry support, consider using a native OpenTelemetry tool.

Alerts and Notifications

New Relic

New Relic’s alerting functions have been flexible and efficient. You can enrich alerts with additional context from New Relic data, correlate incidents, and even perform root cause analysis. What I really appreciate is how it reduces noisy alerts and groups related issues for better insights.

For example, if I want New Relic to notify Slack channel about any latency in a recent deployment, I can easily set up a destination with the necessary API keys. Notifications can also be sent to services like Slack, Jira, ServiceNow, and email, providing versatile options for seamless integration.

Splunk

Splunk’s alerting system is also robust and user-friendly. It lets you set custom performance thresholds and send alerts via Slack, and email. Splunk also integrates smoothly with collaboration tools like Slack and Microsoft Teams, providing real-time notifications with detailed data and complex conditions.

New Relic vs Splunk : It's a tie, both New Relic and Splunk offer robust features but have their own limitations.

UI and UX

New Relic has a much more user-friendly interface compared to Splunk. Navigating through New Relic is easy, even if you are new to it, and customizing dashboards is pretty straightforward. The overall design just feels cleaner and more intuitive.

On the other hand, I found Splunk's interface a bit more complicated. It has a lot of features, but the learning curve is steeper, and it took me longer to get comfortable with it. Overall, I would say New Relic offers a better and smoother user experience.

New Relic vs Splunk: New Relic is better with its easy-to-use and intuitive interface.

Documentation

New Relic’s documentation is really straightforward. Each topic is explained clearly, so you don't need to jump around to understand something. For example, when I was setting up a dashboard, everything I needed was right there in one section.

With Splunk, I found that I often had to switch between different topics to get the full picture, which made things a bit more complicated.

New Relic vs Splunk: Overall, New Relic's documentation is more organized and easier to follow, saving time and effort.

Pricing

New Relic:

New Relic offers both a free tier and various paid plans. Its pricing model is usage-based, providing 100GB of free data ingestion each month. After exceeding this limit, costs are $0.30 per GB or $0.50 per GB, depending on your plan. Additionally, New Relic features four different pricing tiers, including a free forever plan.

Splunk:

Splunk’s pricing model is complex and can be expensive. It provides several pricing options, such as workload, entity, ingest, and activity-based pricing. The exact cost isn’t displayed on the website, so users need to reach out to Splunk’s sales team for detailed pricing information.

New Relic vs. Splunk: Key Takeaways

• Ease of Use: New Relic is more user-friendly and intuitive.
• Log Management: Splunk handles large log volumes better, but requires manual setup.
• APM Features: New Relic offers quicker and simpler setup, while Splunk provides deeper data handling.
• OpenTelemetry: Both tools support OpenTelemetry, but full optimization may need native tools.
• Machine Learning: Splunk excels with advanced machine-learning features.
• Customization: New Relic offers easier dashboard customization.
• Learning Curve: Splunk has a steeper learning curve compared to New Relic.

An alternative to New Relic vs Splunk : Atatus

Atatus is a comprehensive Observability solution that provides an excellent alternative to New Relic and Splunk.

Atatus Application Performance Monitoring (APM) offers in-depth insights into your applications, allowing you to pinpoint performance bottlenecks, resolve issues, and enhance your services. Atatus APM ensures optimal performance and user experience for your applications.

Key Features of Atatus includes,

• Comprehensive Observability: Atatus combines Metrics, Logs, and Traces in one platform for holistic visibility.
• Log Management: Atatus provides a flexible log management solution with an out-of-the-box UI.
• User-Friendly Interface: Intuitive setup and easy-to-navigate interface reduce complexity.
• Transparent Pricing: No hidden costs or special pricing for custom metrics.
• Free Trial: Explore features risk-free with a 14-day trial.

Don't just take my word for it - listen to what our customers have to say. Look at these G2 reviews.

New to Atatus? Try it out with a 14 days free trial.

Every application tells a story of user intent, system behavior, and business impact. To truly understand how your application performs, you need to go beyond logs and errors. You need metrics that provide actionable visibility across your stack. Application performance metrics are the foundation for delivering high-quality digital experiences, and they empower DevOps teams, developers, engineers, and site reliability engineers (SREs) to respond faster, scale smarter, and continuously improve.

In this guide, we’ll explore the top 15 metrics you should be tracking, how to use them effectively, and why Atatus is an outstanding choice for application performance monitoring (APM).

Table of Contents:

1. What Exactly Are Application Performance Metrics?
2. Why Are Application Performance Metrics Important?
3. Top 15 Application Performance Metrics for Developers and SREs
4. Best Practices for Implementing APM Metrics
5. How to Track Application Performance Metrics?
6. Real-World Examples and Case Studies for Application Metrics
7. Security and Compliance Considerations
8. Conclusion: Your Metrics-Driven Future Starts with Atatus APM
9. FAQs on Application Metrics

What Exactly Are Application Performance Metrics?

Application performance metrics are measurable indicators that help teams understand how their applications behave in real-world environments. These metrics cover everything from speed (like response time and latency) to user behavior (like session length and conversion rates) giving DevOps teams, developers, and SREs the visibility they need to maintain high-performance software.

When you monitor the right metrics, you can detect issues faster, reduce downtime, improve user experience, and align application performance with business goals. They form a key part of application performance monitoring (APM) and application observability strategies.

Why Are Application Performance Metrics Important?

Monitoring application performance is no longer optional. Applications today run on distributed infrastructure, support thousands (or millions) of users, and need to meet high availability and reliability expectations.

Here’s why tracking application metrics matters:

• Catch issues early: Metrics like error rates or CPU usage help spot problems before they become incidents.
• Improve application health: Performance insights allow you to fine-tune systems for stability and speed.
• Support SLAs and SLOs: Uptime, latency, and Apdex scores help meet contractual obligations.
• Drive better user experiences: Metrics such as session duration and retention rate reflect how users interact with your app.
• Enable business growth: Performance issues directly affect conversion rate and customer satisfaction.

Top 15 Application Performance Metrics for Developers and SREs

For any high-performing DevOps or SRE team, tracking the right application performance metrics is critical to maintaining stability, scalability, and a smooth user experience. These 15 metrics offer deep insights into your application's health, highlight areas of improvement, and help prevent downtime or performance degradation before they impact your users or your business.

Each of these metrics provides a different lens on application latency, system load, reliability, and user engagement giving developers and site reliability engineers the visibility they need for effective application performance monitoring and proactive incident response.

1. Error Rates

This metric monitors how frequently your application fails, such as HTTP 5xx errors, failed API responses, or thrown exceptions.

Why it matters: A rising error rate often signals critical code or infrastructure issues. By catching these early, you reduce downtime, improve user trust, and prevent error storms that could crash your application. It’s an essential signal for maintaining application reliability and backend stability.

2. Response Time

Measures the time it takes for your application to respond to user actions from frontend clicks to backend service calls.

Why it matters: Application latency directly impacts user satisfaction. Even slight delays can cause drop-offs or decreased engagement. Response time is one of the most visible performance indicators for both users and internal teams.

3. Throughput

Measures the number of requests or transactions your system processes per second or minute.

Why it matters: High throughput means your app can handle heavy loads. Low or fluctuating throughput may suggest system bottlenecks or code inefficiencies, especially during high-traffic periods.

4. Resource Usage (CPU & Memory)

Monitors how much processing power (CPU) and memory (RAM) your application consumes.

Why it matters: Excessive CPU or memory usage can degrade performance or even crash the application. It’s essential for resource planning, detecting memory leaks, and maintaining system stability across environments.

5. Request Rate

Indicates how many requests are hitting your application over a given period.

Why it matters: Helps identify usage trends and peak load times. Tracking this metric is vital for scaling strategies, autoscaling setups, and traffic spike mitigation.

6. User Satisfaction (Apdex Score)

Apdex (Application Performance Index) scores quantify how satisfied users are with application response times based on pre-set thresholds.

Why it matters: It simplifies complex latency data into a user-focused score giving teams a performance health snapshot from the end-user’s perspective.

7. Churn Rate

Measures the percentage of users who stop using your application over a set period.

Why it matters: If your app performs poorly, users leave. High churn rates may be tied directly to slow load times, crashes, or inconsistent availability. This is where technical performance affects business performance.

8. Session Duration

Tracks how long users actively interact with your app in a single session.

Why it matters: Short sessions may indicate frustration, crashes, or unengaging UX often caused by backend slowness or frontend latency. It’s a key user experience metric that links performance to engagement.

9. Conversion Rate

Monitors the percentage of users who complete a goal like signing up, purchasing, or subscribing.

Why it matters: Application performance directly influences revenue. If your app is slow or buggy, users won’t convert. A high-performance app boosts both conversions and brand credibility.

10. Uptime (Application Availability)

Shows how often your app is up, accessible, and functioning correctly.

Why it matters: Even brief downtimes can break trust and lose customers. Monitoring application uptime is vital for meeting SLA requirements and ensuring business continuity.

11. Database Query Performance

Evaluates the execution speed and efficiency of queries within your database systems.

Why it matters: Poor query performance leads to slow transactions and delays throughout the stack. This metric is crucial for backend application metrics and optimizing database-heavy services.

12. Garbage Collection (GC) Time

Measures how much time your system spends reclaiming unused memory, especially in environments like Java, .NET, or Node.js.

Why it matters: Excessive or long GC pauses can freeze apps and degrade user experience. It’s a key metric for identifying performance degradation in memory-managed environments.

13. Retention Rate

Indicates how many users return to your app after their first session.

Why it matters: High retention often signals strong app performance, while a drop can mean bugs, slowness, or reliability issues. Retention is a strong metric for long-term product-market fit and user satisfaction.

14. Session Length

Tracks the average amount of time users spend in your application per session.

Why it matters: Longer sessions usually indicate higher engagement and usability. Short or abandoned sessions often point to application errors, crashes, or performance frustrations.

15. Network Latency

Represents the time it takes for data to travel between the client (user) and the server (application).

Why it matters: High network latency affects all other performance metrics even if your code is efficient. It’s especially important for globally distributed users or mobile-heavy apps.

Tracking these 15 key metrics for measuring application performance is essential for modern engineering teams that aim to ship stable, fast, and user-friendly applications. Whether you’re deploying new features, troubleshooting incidents, or planning your next scaling phase, they lay the foundation for real-time monitoring, performance benchmarking, and issue resolution workflows.

How APM Metrics Relate to Each Other?

Monitoring metrics in isolation misses the context that makes them actionable. Here are the most important metric relationships that engineering teams should track together:

Best Practices for Implementing APM Metrics

• Define key metrics early: Don’t wait for issues, plan which metrics matter most based on app type and users.
• Set clear baselines and thresholds: Know what's normal for response time, throughput, and error rates.
• Alert intelligently: Avoid alert fatigue by setting thresholds only where action is truly required.
• Correlate metrics: Use a unified dashboard to connect application metrics with infrastructure data.
• Review trends, not just spikes: Long-term patterns reveal more than isolated incidents.

How to Track Application Performance Metrics?

The most efficient and scalable way to track application performance metrics is by using a dedicated Application Performance Monitoring (APM) tool. APM tools automatically collect key metrics such as response time, error rates, CPU usage, network latency, Apdex scores, and more without the need for manual instrumentation or custom dashboards.

An APM solution like Atatus provides end-to-end visibility across your application stack, including frontend, backend, databases, and infrastructure.

With real-time monitoring, intelligent alerting, and easy-to-use dashboards, it empowers developers, DevOps teams, and SREs to detect anomalies, resolve issues faster, and ensure optimal user experience.

Instead of juggling logs, metrics, and traces in separate systems, an APM tool brings them all together, so you can focus on improving performance, not chasing problems.

Real-World Examples and Case Studies for Application Metrics

• E-commerce Platform

An online retail company noticed increasing cart abandonment rates. Using APM metrics, they correlated spikes in response time and database query latency during flash sales. Optimizing slow queries and adding caching layers improved load times and led to an increase in conversion rates.

• Financial Services Application

A digital banking app was struggling with intermittent downtime. By tracking error rates, uptime, and garbage collection time, engineers discovered that memory leaks were causing frequent service restarts. Fixing memory allocation reduced downtime and boosted their Apdex score significantly.

Security and Compliance Considerations

While tracking performance metrics, security cannot be overlooked:

• Audit logs and error logs should not expose sensitive data.
• Use metrics to monitor unusual spikes in traffic that may indicate DDoS attacks.
• Ensure data collected for metrics complies with GDPR, SOC 2, or HIPAA as needed.
• Monitoring tools must support role-based access control (RBAC) and encryption.

By integrating application metrics into your security posture, you not only ensure performance but also maintain trust.

Conclusion: Your Metrics-Driven Future Starts with Atatus APM

Choosing the right application performance monitoring tool can transform your app from just working to truly delivering. From latency to retention, each metric tells a story, one you can't afford to ignore if you're serious about uptime, speed, and user experience.

If you're looking for a monitoring solution that combines clarity, depth, and ease of use, Atatus is the best APM tool available built for engineering, DevOps, and SRE teams who want detailed, actionable insights into their applications.

Here’s why Atatus APM stands out:

• Granular visibility: Track everything from response time and GC to user satisfaction and memory leaks.
• Real-time dashboards: Monitor and correlate application, infrastructure, and user metrics in one unified platform.
• Built for modern stacks: Whether it's Node.js, PHP, Java, Python, or containers, Atatus integrates seamlessly.
• Customizable alerts: Stay ahead of issues with intelligent alerts based on thresholds, trends, or anomalies.
• Affordable and scalable: Transparent pricing and high data retention without enterprise-level costs.

Whether you're building a new product or maintaining complex microservices, Atatus gives you the power of application observability without the bloat.

FAQs on Application Metrics

What are the most important APM metrics during high-traffic sale events for e-commerce platforms?
Monitor throughput, conversion rate, checkout latency, DB query time, and CPU/memory usage. Real-time metrics help detect traffic-related bottlenecks and prevent lost revenue.

How do I know if slow database queries are affecting my application performance?
Watch for rising query times, response delays, timeouts, and falling Apdex scores. APM tools with tracing show which queries slow down your app.

What metrics indicate memory leaks in a long-running application?
Look for steadily rising memory usage, frequent or long GC events, and increasing latency or errors. Combined, these point to a memory leak.

What role do application performance metrics play in CI/CD deployments?
Metrics like post-deploy response time, error spikes, and API latency reveal regressions. APM tools validate app health automatically after each release.

Which APM metrics are most useful when debugging an intermittent outage or spike in errors?
Correlate error rate, request rate, CPU load, and dependency latency. Traces reveal whether the root cause is internal or third-party.

How can application metrics help detect business-impacting issues before they reach the end user?
Track early warning signs like error spikes, GC pauses, or rising backend latency. APM tools like Atatus alert teams before users are affected.

How can I align application performance metrics with SLOs and SLAs in a production environment?
Use uptime, error budgets, response time thresholds, and Apdex scores to monitor SLO compliance. APM dashboards track these automatically over time.

As we move toward 2026, observability is evolving from an engineering luxury to an operational necessity. Modern applications span microservices, containers, APIs, and data pipelines and when something breaks, users expect instant recovery.

That urgency is fueling rapid market growth. According to Market.us, the Global Data Observability Market is projected to reach several billion dollars by 2033, growing at a CAGR exceeding 20% between 2024 and 2033. Enterprises are investing heavily in tools that help teams detect, analyze, and resolve issues faster while optimizing performance and controlling costs.

The trend is clear: observability is becoming a key pillar of software reliability, similar to how CI/CD became the backbone of deployment pipelines. As automation and OpenTelemetry adoption rise, observability tools will define how efficiently organizations scale in 2026 and beyond.

What's in this guide?

1. What Are Observability Tools?
2. How Observability Helps Development Teams?
3. Top 15 Observability Tools to Use in 2026
4. How to Choose the Right Observability Tool
5. Which Observability Tool Should You Choose in 2026?
6. Final Thoughts: Observability in 2026 and Beyond
7. Observability in 2026: Common Questions and Expert Answers

What are Observability Tools?

Observability tools collect, correlate, and visualize the three critical types of telemetry data including metrics, logs, and traces to help teams understand how complex systems behave in real time. Instead of relying on isolated monitoring dashboards, observability connects the dots across infrastructure, code, and user experience.

A modern observability platform typically offers:

• Metrics: Quantitative time-series data such as CPU usage, request rate, or latency.
• Logs: Textual event data that captures detailed execution and error information.
• Traces: End-to-end request journeys across distributed systems, showing where latency or failure originates.

Together, these enable developers, DevOps engineers, and SREs to pinpoint issues, measure performance, and maintain uptime at scale.

How Observability Helps Development Teams?

Engineering and DevOps teams use observability platforms to move from reactive troubleshooting to proactive performance optimization. Here’s how it helps:

• Accelerated Root-Cause Analysis: Instead of guessing, engineers can follow a trace from frontend click to backend query, identify slow endpoints, and correlate with logs in seconds.
• Reduced MTTR (Mean Time to Resolution): Correlating incidents across services and environments shortens downtime and prevents repeat outages.
• Improved Developer Productivity: With unified dashboards and contextual alerts, developers spend less time firefighting and more time building.
• Data-Driven Performance Optimization: Observability platforms highlight slow database calls, memory leaks, or inefficient dependencies that affect user experience.
• Reliable Releases and Faster Feedback: Teams can observe the impact of new deployments immediately and roll back before users notice regressions.

Top 15 Observability Tools to Use in 2026

Below is a detailed comparison of the top 15 observability tools leading the market in 2026. The focus is on key capabilities, pricing direction, and where each platform fits best.

Top Observability Tools to Use in 2026

#1 Atatus- Next-Gen Observability. Unified, Scalable, Insight-Driven

Atatus provides developer-friendly monitoring that links application performance to code and user impact. Built to be simple to adopt and fast to extract value from, Atatus helps teams find the root cause quickly and instrument their stack without lengthy onboarding.

Core capabilities

• Unified monitoring across apps, servers, and front-end: Single-pane view across APM, RUM, and server metrics so teams can pivot from a slow endpoint to the exact trace and logs that explain it.
• APM dashboard designed for developers: Transaction traces, span details, slow SQL and external call breakdowns that surface the offending code path and the related deployment or release.
• End-to-end visibility into logs: Correlate traces to logs without manual linking so you can move from “where” to “why” in fewer clicks.
• Custom dashboards & alerting: Build focused dashboards for services, teams, or SLOs and create alerts that reflect business impact rather than raw thresholds.
• Flexible pricing: Transparent tiers and usage-based options suitable for startups and scaling teams; pricing designed to reduce surprise costs as volume grows.

Who it’s best for?
Engineering teams that want quick time to value, strong developer UX, and a clear path from code → trace → fix. Atatus suits product teams that prefer practical observability workflows over enterprise configuration heavy approaches.

Why teams choose Atatus?

• Fast onboarding and focused developer workflows.
• Clear trace-to-log correlation to speed incident resolution.
• Pricing designed with growing engineering teams in mind.
• Atatus is ideal for small to mid-sized businesses and fast-growing tech teams who want enterprise-level observability without enterprise-level costs.

#2 Datadog - Enterprise Observability Powerhouse

For organizations with hybrid and multi-cloud environments, Datadog is one of the most comprehensive observability solutions. It’s designed to handle complex data pipelines, large distributed systems, and deep AI-driven insights.

Key Features
Datadog unifies metrics, traces, logs, and security analytics under a single platform. It provides built-in APM, RUM, infrastructure, and network performance modules, alongside advanced anomaly detection and forecasting. Teams can visualize dependencies with real-time service maps and leverage automation for incident response.

Pricing
Datadog uses a modular pricing structure, charging separately for APM, logs, and infrastructure. Costs typically scale with data ingestion and host count, making it one of the more premium options.

Pros

• Exceptional coverage and integration ecosystem (600+ tools).
• Best suited for large enterprises managing multi-cloud workloads.
• AI-driven detection and automated remediation options.

Cons

• Expensive as data ingestion scales.
• Steeper learning curve for new users.
• Dashboard clutter can be overwhelming for smaller teams.

Datadog remains a top choice for enterprises that prioritize breadth, depth, and automation in observability.

3. Dynatrace - Intelligent Automation for Observability

For teams that want AI-driven insights and autonomous performance management, Dynatrace is a leader. Its core value lies in combining full-stack observability with deep automation.

Key Features
Dynatrace offers full-stack observability across infrastructure, applications, and user experience. Its Davis AI engine automatically identifies anomalies and provides root-cause analysis without manual setup. The platform covers logs, metrics, traces, and real-user monitoring under one interface.

Pricing
Dynatrace pricing is usage-based, typically higher than average, targeting enterprise budgets. Licensing is based on host units and monitored entities.

Pros

• Powerful automation with minimal manual tuning.
• Deep AI-driven diagnostics reduce MTTR significantly.
• Excellent enterprise-scale reliability.

Cons

• High entry cost.
• Requires commitment to the Dynatrace ecosystem.
• Interface complexity can be challenging for smaller teams.

Dynatrace fits enterprises prioritizing automation and deep diagnostics over pricing flexibility.

Wondering if Dynatrace is really worth the price? Check out how Atatus delivers powerful monitoring with transparent pricing and no surprise bills.

4. New Relic - Developer-First Observability

For development-heavy organizations, New Relic provides a broad observability platform focused on developer empowerment.

Key Features
New Relic brings together APM, logs, infrastructure, browser, and mobile monitoring into one UI. The NRQL (New Relic Query Language) allows custom data exploration, enabling engineers to query metrics easily. Distributed tracing and real-time analytics make it easy to connect performance data with code changes.

Pricing
New Relic follows a pay-as-you-go model - $0.30/GB for data ingestion, with free tiers for light users. It’s more transparent than most legacy tools.

Pros

• Great for developers; simple yet powerful UI.
• Transparent usage-based billing.
• Rich community and integrations.

Cons

• Costs can rise quickly with high data ingestion.
• Some advanced features locked in premium tiers.

New Relic strikes a good balance between flexibility, power, and ease of use which is ideal for developer-centric teams.

5. Splunk Observability Cloud - Analytics-Driven Performance at Scale

For enterprises operating across multi-cloud and hybrid environments, Splunk Observability Cloud delivers unmatched scalability and analytics. It’s built for organizations ingesting massive telemetry volumes who need to connect operational data with business impact.

Key Features
Splunk unifies metrics, traces, and logs into a single pipeline for real-time analytics. It features APM, infrastructure monitoring, RUM, and synthetic testing integrated with Splunk’s analytics engine. Machine-learning capabilities detect anomalies, identify trends, and automate root-cause analysis. Its visualization tools make sense of petabytes of observability data without manual correlation.

Pricing
Splunk uses a usage-based pricing model that scales by data ingestion and retention time. While powerful, it tends to be one of the more expensive options once data volumes grow.

Pros

• Excellent scalability and enterprise reliability.
• Deep analytics with machine-learning support.
• Integrates tightly with the Splunk ecosystem for unified data management.

Cons

• Expensive for high-volume log ingestion.
• Steeper learning curve for setup and optimization.
• May feel excessive for smaller DevOps teams.

Splunk is best for large enterprises needing a single source of truth for analytics, observability, and incident management.

6. Grafana Cloud - Open-Source Power with Managed Simplicity

For teams that love the flexibility of open-source but want managed reliability, Grafana Cloud provides the best of both worlds. It combines the popular Grafana visualization suite with managed Prometheus, Loki, and Tempo backends.

Key Features
Grafana Cloud centralizes dashboards, logs, and traces through native integrations. Developers can build custom observability dashboards powered by open standards (PromQL, Loki queries). It supports alerting, correlation, and team collaboration. Its plugin ecosystem enables integrations with virtually any metric source.

Pricing
Grafana Cloud offers a free tier and affordable pro plans starting around $8–9 per user/month, with scalable pricing for data retention and metrics.

Pros

• Strong open-source foundation and flexibility.
• Cost-effective compared to enterprise tools.
• Excellent dashboarding and visualization capabilities.

Cons

• Requires some expertise in query languages (PromQL, LogQL).
• Limited enterprise-grade automation and AI insights.
• Data ingestion limits at lower tiers.

Grafana Cloud is ideal for teams that want full control and customization without managing infrastructure themselves.

7. Elastic Observability - Unifying Logs, Metrics, and APM

Elastic Observability, part of the Elastic Stack (Elasticsearch, Logstash, Kibana), offers a robust, extensible observability solution for organizations already invested in Elastic’s data platform.

Key Features
Elastic Observability consolidates logs, metrics, and traces into Elasticsearch, offering advanced search and analytics through Kibana dashboards. Its APM and RUM modules help pinpoint performance issues at the code level. Elastic’s machine-learning features detect anomalies and automate alerting across environments.

Pricing
Elastic follows a usage-based pricing for its managed cloud (Elastic Cloud) and is free for self-hosted setups, though infrastructure costs apply.

Pros

• Highly flexible and extensible via open APIs.
• Deep analytics and search capabilities.
• Strong ecosystem for self-hosting and customization.

Cons

• Requires setup and tuning for optimal performance.
• Scaling can be complex without managed services.
• UI not as beginner-friendly as newer SaaS solutions.

Elastic Observability suits teams that already rely on the Elastic Stack and want to extend it into full-stack observability.

8. Sumo Logic Observability - Cloud-Native Simplicity for DevSecOps

Sumo Logic Observability helps DevOps and security teams maintain unified visibility in modern cloud environments. It’s purpose-built for microservices, containers, and event-driven architectures.

Key Features
Sumo Logic’s platform correlates logs, metrics, and traces across distributed services. It includes real-time alerting, dashboards, anomaly detection, and log analytics powered by machine learning. Its unified model helps DevSecOps teams monitor both performance and security events simultaneously.

Pricing
Sumo Logic uses tiered pricing based on ingestion and data retention, typically starting around $3–4 per GB. There’s also a free trial for small teams.

Pros

• Fast setup with fully managed SaaS architecture.
• Great fit for cloud-native and containerized applications.
• Integrates easily with AWS, GCP, and Kubernetes.

Cons

• Pricing can grow steeply with large data volumes.
• Some dashboards have limited customization.
• Not as deep in APM as dedicated solutions like Atatus or New Relic.

Sumo Logic stands out for its simplicity and quick deployment which is a practical choice for modern cloud monitoring.

9. AppDynamics (Cisco) - Business-Driven Application Monitoring

AppDynamics targets enterprises that need to connect technical performance with business impact. Acquired by Cisco, it’s widely used in regulated industries and large organizations.

Key Features
AppDynamics tracks business transactions end-to-end, linking code-level performance metrics to business KPIs. It offers deep diagnostics, user experience monitoring, infrastructure mapping, and strong visualizations of performance bottlenecks.

Pricing
AppDynamics pricing is license-based, typically around $60 per agent/month, with enterprise plans varying by scale and deployment.

Pros

• Ideal for large, complex enterprise environments.
• Clear mapping between application health and business outcomes.
• Strong support for hybrid and on-prem infrastructures.

Cons

• Expensive for smaller teams.
• Interface and setup can feel dated compared to modern SaaS options.
• Slower innovation cycle post-acquisition.

AppDynamics remains a solid enterprise APM, particularly when business context visibility is critical.

10. LogicMonitor - Infrastructure-Focused Observability

LogicMonitor offers deep infrastructure monitoring, particularly valuable for hybrid environments combining legacy and cloud systems.

Key Features
It automatically discovers devices, servers, and applications, monitoring performance metrics in real time. The AI-driven alerting and anomaly detection systems help IT teams predict issues before outages. The platform also provides clear network topology mapping and hybrid visibility across environments.

Pricing
LogicMonitor charges per monitored device, averaging $20–25 per device/month, depending on scale and integrations.

Pros

• Excellent for hybrid and on-prem infrastructure.
• Easy to deploy with minimal configuration.
• Strong visualization and dependency mapping tools.

Cons

• Limited APM and tracing depth compared to developer-centric tools.
• Focused more on IT operations than developer observability.

LogicMonitor is best suited for IT and infrastructure teams needing hybrid visibility rather than code-level performance data.

11. Prometheus - Open-Source Metrics Pioneer

Prometheus remains one of the most widely adopted open-source monitoring systems, essential in Kubernetes environments.

Key Features
It’s a time-series database designed to collect and query metrics with high precision. Using PromQL, teams can perform advanced queries and build dashboards (often in Grafana). It’s a core component of the CNCF observability stack and integrates seamlessly with exporters and Kubernetes.

Pricing
Free and open-source. Hosting, scaling, and alerting infrastructure incur additional costs when self-managed.

Pros

• Ideal for Kubernetes and microservices.
• High flexibility for metric collection and analysis.
• Strong community and ecosystem support.

Cons

• Lacks native log and trace correlation.
• Requires manual scaling and data retention configuration.
• Not a full observability platform without additional tools (Grafana, Loki, Tempo).

Prometheus is excellent for engineers who want granular metric visibility and control.

12. Sentry - Error Tracking for Developers

Sentry focuses on application error monitoring and performance tracking which is purpose-built for developers shipping high-velocity applications.

Key Features
Sentry provides real-time error tracking, stack trace analysis, and release monitoring. It supports popular frameworks (React, Node.js, Django, Go, etc.) and correlates errors with deployments to identify regressions. Performance monitoring modules reveal slow queries, transactions, and API bottlenecks.

Pricing
Sentry offers a free plan and paid tiers starting at $26/month, depending on event volume.

Pros

• Excellent developer experience and clean UI.
• Instant context for debugging.
• Strong integration with CI/CD workflows.

Cons

• Focused mainly on error tracking, not full observability.
• Lacks infrastructure and log-level visibility.

Sentry is perfect for dev teams prioritizing real-time debugging and release stability.

13. Instana (IBM) - Smart Automation for Cloud-Native Observability

Instana, now part of IBM, delivers automated instrumentation and granular tracing for microservices and Kubernetes environments.

Key Features
Instana auto-discovers services, dependencies, and infrastructure components. Its high-frequency data collection (every second) ensures extremely detailed visibility. The AI-driven root-cause analysis engine highlights performance anomalies automatically.

Pricing
Starts around $75 per host/month, with enterprise pricing for large environments.

Pros

• Excellent automation and high-resolution data.
• Great for containerized and microservice architectures.
• Minimal manual setup required.

Cons

• Expensive for smaller teams.
• UI can feel dense due to data volume.
• Some integration depth depends on IBM stack.

Instana suits modern enterprises running dynamic microservices at scale.

14. Zabbix - Proven Open-Source Infrastructure Monitoring

Zabbix remains one of the most trusted open-source monitoring platforms for infrastructure-heavy organizations.

Key Features
It offers agent and SNMP-based monitoring for servers, devices, and networks. Users can create custom dashboards, triggers, and alerts. Zabbix can monitor thousands of nodes simultaneously with the right tuning.

Pricing
Completely free to use. Costs come from hosting, setup, and personnel. Paid enterprise support is available via partners.

Pros

• No licensing cost; complete control.
• Mature, stable, and widely adopted.
• Flexible for organizations that prefer on-premises management.

Cons

• No built-in tracing or log correlation.
• Requires significant configuration effort.
• Steeper learning curve for smaller dev teams.

Zabbix is a reliable choice for organizations prioritizing self-hosted, cost-efficient infrastructure monitoring.

15. Chronosphere - Scale-Ready Metrics Platform

Chronosphere is purpose-built for companies managing thousands of microservices, focusing on controlling observability costs and managing metric cardinality.

Key Features
It offers high-volume metric ingestion, retention control, and cost optimization. Its dynamic dashboards and metric aggregation let teams keep visibility while minimizing data waste. Chronosphere is architected for massive scale and reliability.

Pricing
Enterprise-only pricing, tailored to ingestion volume and data retention.

Pros

• Excellent performance for large-scale observability.
• Cost control features for managing metric explosion.
• Highly resilient and scalable infrastructure.

Cons

• Complex to deploy for smaller orgs.
• Limited log and trace features.
• High entry price.

Chronosphere is ideal for hyper-scale engineering teams where controlling metric costs is as critical as observability itself.

How to Choose the Right Observability Tool?

Selecting the right observability platform isn’t about chasing the most popular brand; it’s about aligning capabilities with your technical environment, budget, and long-term operational goals.

Here’s a deeper look at what to consider before making a decision.

Specific Requirements

• Start by defining your team’s core priorities.
• If your goal is deep visibility into application behavior, APM-focused solutions like Atatus, New Relic, or Dynatrace deliver rich transaction traces, database insights, and error analytics.
• For infrastructure and network observability, LogicMonitor, Grafana, or Zabbix are better choices since they specialize in metrics and system-level health monitoring.
• Hybrid teams often benefit from platforms that bridge both.
• For example, Atatus, which integrates APM, logs, and infrastructure monitoring under one interface.

Scalability and Cost

• Every observability tool scales differently and the pricing models vary just as widely.
• For enterprises ingesting massive telemetry data, Splunk or Datadog handle large-scale workloads efficiently, albeit at a premium.
• For cost-conscious startups and mid-sized teams, Atatus, Grafana Cloud, or Elastic provide robust capabilities at predictable, lower price points.
• Always evaluate both the data retention policies and billing transparency, as costs can escalate quickly once your environment expands.

Integration and Ecosystem

• A great observability platform fits seamlessly into your existing workflow.
• Ensure the tool integrates well with your CI/CD pipelines, cloud providers (AWS, Azure, GCP), Kubernetes clusters, and alerting systems like Slack, PagerDuty, or Opsgenie.
• Atatus, for example, supports plug-and-play integrations that simplify observability setup without disrupting ongoing DevOps or deployment processes.

Team Skills and Management Model

• The tool you pick should match your team’s technical maturity.
• Developer-friendly SaaS platforms such as Atatus, Sentry, and New Relic allow teams to get started quickly without managing servers or custom dashboards.
• Conversely, self-managed solutions like Prometheus, Zabbix, or Elastic Stack provide full control but they demand ongoing maintenance, tuning, and scaling expertise.

Which Observability Tool Should You Choose in 2026?

As observability continues to evolve into a core engineering discipline, the focus should be on fit, not flash. The right tool is one that scales with your system’s complexity while keeping costs and workflows manageable.

• For cost-efficient, unified observability: Choose Atatus. It delivers end-to-end visibility from APM to logs, infrastructure, and end-user monitoring in a single, affordable platform.
• For enterprise automation and AI-assisted insights: Dynatrace or Splunk remain strong contenders.
• For open-source flexibility and community-driven innovation: Go with Grafana Cloud or Prometheus.
• For deep debugging and developer-centric workflows: Sentry and New Relic shine.

But heading into 2026, Atatus stands out as the most balanced choice which combining performance monitoring depth, affordability, and ease of use in a way that aligns with modern engineering priorities.

Final Thoughts: Observability in 2026 and Beyond

The observability market is entering a mature phase. What used to be fragmented and separate tools for APM, logs, and infrastructure is now converging into cohesive platforms.
Organizations are moving beyond basic monitoring to reliability-driven engineering, where visibility and cost control are equally important.

Platforms like Atatus represent this new wave: offering complete observability without complexity, giving teams the clarity, control, and scalability needed to keep digital systems resilient and budgets predictable.

Observability in 2026: Common Questions and Expert Answers

1) What features should you look for in an observability tool?

The best observability tools offer unified dashboards, APM, log management, real-user monitoring, alerting, and integrations. Scalability, pricing transparency, and ease of use are equally critical when evaluating a solution.

2) Why is observability important for DevOps teams?

Observability helps DevOps teams detect performance bottlenecks early, understand system dependencies, and automate issue resolution. It supports faster release cycles and ensures consistent reliability across microservices and CI/CD pipelines.

3) Can small teams or startups use observability tools?

Yes. Modern observability platforms like Atatus and Grafana Cloud offer flexible pricing and lightweight setups suitable for small teams. They allow startups to gain full visibility and scale monitoring as their infrastructure grows.

4) What is the difference between APM and observability?

Application Performance Monitoring (APM) focuses on tracking application performance metrics like latency and throughput. Observability extends beyond APM that combining logs, metrics, and traces to understand why performance issues occur across the entire system.

5) What industries benefit most from observability tools?

Observability tools are essential for SaaS, eCommerce, fintech, gaming, and healthcare industries. Any sector that relies on uptime, scalability, and real-time user experience to maintain competitive performance.

Continue Exploring – More Tools That Matter

• Top Application Performance Monitoring Tools
• 13 Best Performance Monitoring Tools for Java
• Serverless Monitoring Tools
• PHP Monitoring Tools
• Best Monitoring Tools for Node.js Application
• API Analytics Tools

IT budgets are getting squeezed, yet teams are expected to deliver faster releases, higher reliability and tighter security. Observability has become one of the few levers that directly influences IT cost reduction because it gives teams the ability to understand exactly what’s consuming resources, wasting storage, dragging performance, and inflating operational workload.

In this guide, you’ll learn seven evidence-backed strategies that leading engineering teams use to cut expenditure. We’ll break down how unified monitoring, smarter data storage decisions, preventive operations and automated insight pipelines together trim infrastructure bills, reduce personnel overhead, and raise efficiency across the entire digital stack.

What's in thie guide?

1. What Does Observability Do to Cut IT Costs?
2. Top 7 Ways Observability Drives IT Cost Savings
3. How to Prioritise Cost-Reduction Steps for Your Team?
4. Why Teams Choose Atatus to Cut Observability Costs?

What Does Observability Do to Cut IT Costs?

Observability cuts IT costs by giving teams precise visibility into how infrastructure, services and workloads behave. When engineering teams can see logs, metrics and traces together, they’re able to reduce waste, prevent incidents, and operate more efficiently. These insights translate into very real savings across multiple layers of your stack.

Here’s what that looks like in practice.

Identifies hidden infrastructure waste:

Most teams significantly over-provision compute and storage simply because they don’t know what’s actually being used. Observability reveals idle workloads, inefficient services, trending memory spikes, redundant APIs, and noisy components that drive cost.

It’s common for teams to discover 15–40% unused capacity once they see real usage trends.

Lowers incident-related losses:

Incidents have direct financial impact:

• Engineering hours spent diagnosing issues
• Lost revenue from slow or broken user journeys
• SLA credits
• Reputation damage

Reducing MTTR and increasing MTBF protects against these losses.

Improves data retention efficiency:

Telemetry data expands rapidly. Logs, in particular, can explode in volume. Observability helps:

• Identify low-value logs
• Reduce retention windows
• Archive cold logs
• Prune redundant traces
• Remove high-cardinality metrics

Teams often cut storage spend by 25–50% after rethinking data policies.

Eliminates redundant monitoring tools:

Multiple monitoring tools create unnecessary spend on licensing, ingestion, training and maintenance. Observability platforms that unify telemetry reduce both direct and indirect costs. Enterprise SaaS management platform CloudNuro enhances tool-spend visibility by centralizing SaaS subscriptions, unused licenses, and redundant costs across your stack, helping engineering and finance teams optimize spend and eliminate unnecessary SaaS licenses alongside observability savings.

Boosts cross-team efficiency:

Observability aligns dev, ops and product teams around shared truth. Faster decisions = fewer delays, fewer escalations and fewer costly missteps.

Top 7 Ways Observability Drives IT Cost Savings

Strategy 1: Consolidate Monitoring Tools Into a Single Observability Platform

The problem: Most engineering teams run 3–6 separate tools for logs, APM, metrics, real user monitoring, synthetics, and infrastructure monitoring. Each requires its own onboarding, maintenance, integrations, and vendor contract.

Why it costs you: Multiple licensing fees, duplicated data pipelines, parallel storage costs, and engineering hours spent keeping tools synchronized. Tool sprawl also slows incident investigations, engineers waste time switching contexts between dashboards.

How observability helps: A unified observability platform centralizes all telemetry including logs, traces, metrics, and user sessions into a single interface with automatic correlation. This removes redundant vendor contracts and reduces the operational overhead of managing multiple integrations.

Realistic savings: Teams consolidating from 4+ tools to a single platform typically report 20–50% reduction in total monitoring-related licensing spend, plus 15–30% reduction in engineering time spent on tool maintenance.

Strategy #2: Optimize Data and Storage Spend

The problem: Telemetry volume grows unchecked as services scale. Logs balloon with verbose debug output, traces capture every request including low-value ones, and high-cardinality metrics accumulate across hundreds of services.

Why it costs you: Storage is one of the fastest-growing IT expenses. Hot storage for recent telemetry data is expensive. Retaining noisy, low-value logs and duplicated traces in expensive storage tiers is waste that most teams don't measure until costs have already escalated.

How observability helps: Observability platforms surface which services generate the most log volume, which traces add no debugging value, and which metrics have excessive cardinality. Teams can apply: retention policies by service criticality, trace sampling to reduce volume without losing signal, log filtering to drop known-noisy events, and cold-archiving for compliance data.

Realistic savings: Applying telemetry data policies consistently delivers 20-40% log volume reduction and 25-50% lower storage costs in most environments, with query performance improvements as a secondary benefit.

Strategy 3: Reduce Mean Time to Resolution (MTTR)

The problem: Delayed detection and slow triage stretch incidents beyond their necessary duration. When logs, metrics, and traces live in separate tools, engineers spend the first 30–60 minutes of an incident just gathering context rather than fixing the problem.

Why it costs you: Every minute of downtime has a measurable cost: engineering hours, user impact, potential SLA credits, and revenue loss from degraded user journeys. Gartner estimates the average cost of IT downtime at $5,600 per minute for enterprise environments. Even for smaller teams, a 2-hour incident costs thousands in engineering hours alone.

How observability helps: When logs, metrics, and traces are correlated in a single view, root cause identification moves from hours to minutes. Dependency maps show which upstream service is degrading. Timeline views pinpoint exactly when an anomaly began and which deployment or config change preceded it.

Realistic savings: A 30–60% MTTR reduction is achievable for teams moving from siloed tools to unified observability. For a team handling 10 incidents per month averaging 90 minutes each, a 50% MTTR reduction saves roughly 75 engineering hours per month.

Strategy 4: Increase Mean Time Between Failures (MTBF)

The problem: Recurring incidents happen because teams fix symptoms rather than root causes. Without historical trend data, the same fragile service fails repeatedly under the same conditions like peak traffic, specific deployments, or database contention which draining on-call resources every time.

Why it costs you: Low MTBF means repeated outages, repeated on-call escalations, and repeated fixes. Each recurrence carries the same incident cost as the original. Developer burnout from repeated pager duty is a secondary cost that rarely appears in IT budgets but significantly impacts team productivity and retention.

How observability helps: Historical trend analysis reveals patterns: which services fail under load, which deployments correlate with error spikes, and which infrastructure components show degradation before failure. Reliability dashboards tracking MTBF over time make systemic fragility visible and prioritizable.

Realistic savings: Every prevented incident saves both the direct incident cost and the follow-on investigation cost. For teams with 3+ recurring incidents per quarter, improving MTBF by 2x typically delivers the equivalent of 1-2 full engineering weeks per quarter in recovered time.

Strategy 5: Shift from Reactive to Preventive Operations

The problem: Most teams only act after an alert fires or a service visibly breaks. Reactive operations mean off-hours escalations, rushed fixes, and inefficient firefighting that interrupts planned engineering work.

Why it costs you: Reactive operations carry a hidden cost multiplier: incidents handled reactively take 3-5x longer to resolve than issues caught early. Off-hours escalations cost more in engineer time and lead to higher error rates due to pressure and fatigue.

How observability helps: Proactive dashboards surface early warning indicators before they become incidents: rising p95 latency, gradual memory drift, error rate upticks on specific endpoints, and unexpected traffic pattern changes. Teams that review these indicators daily can act before users are impacted.

Realistic savings: Preventing a single major incident per month, which conservative estimates value at 3-8 engineering hours plus any user impact that covers the annual cost of most observability platforms. Preventive operations primarily save through avoided incidents and reduced off-hours escalations.

Strategy 6: Build Cross-Team Alignment Around Shared Telemetry

The problem: Development and operations teams often work from different data sources, different dashboards, and different definitions of 'healthy.' When an incident occurs, the first 20-30 minutes are often spent debating whose dashboard is correct rather than diagnosing the issue.

Why it costs you: Fragmented data causes duplicated investigation effort, slower handoffs between teams, and misaligned prioritization. Developers fix symptoms that ops can see are not root causes, and vice versa. Every miscommunication costs time.

How observability helps: A shared telemetry pipeline and unified dashboards give dev, ops, and product teams a single source of truth. When everyone sees the same traces, the same error rates, and the same infrastructure metrics, investigations converge faster and prioritization decisions are grounded in the same data.

Realistic savings: Teams report 20-35% reduction in cross-team investigation overhead after adopting shared observability dashboards. The compounding benefit is faster deployments when teams trust the same data, release confidence increases and deployment frequency improves.

Strategy 7: Track Observability Cost Metrics and Review Them Monthly

The problem: Most teams implement cost-saving changes but never measure whether they worked. Without ongoing tracking, log volume creeps back up, storage tiers drift toward expensive options, and MTTR gradually worsens as the team grows.

Why it costs you: Untracked cost savings erode within 3–6 months. Runaway logging returns, anomalies go unnoticed, and tool overlap re-emerges as new services are onboarded. The absence of a review cadence is the single most common reason IT cost reduction initiatives fail to sustain results.

How observability helps: Monthly cost dashboards showing telemetry volume by service, storage spend trends, MTTR movement, and incident frequency make cost performance visible and accountable. Cost-per-service metrics make individual teams accountable for their resource consumption.

Recommended monthly review metrics:

• Total log volume by service (target: flat or decreasing)
• Storage spend vs previous month
• MTTR trend (target: decreasing quarter-over-quarter)
• MTBF trend (target: increasing quarter-over-quarter)
• Number of monitoring tool licenses active
• Cost per service (normalized to traffic volume)

How to Prioritise Cost-Reduction Steps for Your Team?

A cost-cutting initiative becomes effective only when you know which actions provide the biggest impact in the shortest time. Here is a practical prioritisation framework your team can apply immediately:

Step 1: Start With the Largest Cost Buckets

Most companies spend heavily in these areas:

• Compute (VMs, containers, Kubernetes workloads)
• Storage (logs, traces, metrics, backups)
• Databases
• Networking
• Third-party monitoring tools

Use observability data to identify which services or clusters consume the highest share of your budget.

Step 2: Attack High-Impact Opportunities First

Sort potential improvements by:

• Money saved
• Speed of execution
• Engineering complexity

Examples of quick wins:

• Shortening log retention for non-critical services
• Cutting trace sampling frequency
• Downsizing overly large instances
• Removing stale indexes or unused queries

These move the needle fast.

Step 3: Fix the Noisy Services That Cause Repeated Issues

Some services always break during traffic spikes. Others produce massive volumes of logs. These services quietly drive up cloud and operations spend.

Observability helps identify:

• Top CPU consumers
• High-memory apps
• Services with repeated latency spikes
• Noisy log generators
• DB-heavy endpoints
• Tackling these gives high ROI.

Step 4: Keep Cloud Costs Tied to Engineering Accountability

Dashboards should show:

• Cost per service
• Usage trends
• Cost to serve per customer
• Cost spikes during deployments

This makes teams accountable for their resource usage.

Step 5: Monitor Progress

Cost savings erode if you don’t track them. Add dashboards that show:

• Before vs after usage
• Decreasing log volume
• Reduced MTTR
• Lower DB queries per service
• Weekly visibility prevents cost creep

Why Teams Choose Atatus to Cut Observability Costs?

Once you know where the inefficiencies are, the next step is choosing a platform that helps eliminate them without increasing complexity.

Atatus gives teams clear visibility from a single platform across APM, logs, infrastructure, RUM and uptime. The pricing model is predictable and based on what you actually use, making it far easier to manage spend without sacrificing features.

Key reasons teams use Atatus to keep observability affordable:

• Unified observability replaces multiple tools: You consolidate APM, logs, traces, real user monitoring, infrastructure, and uptime into one platform. This instantly removes 2–5 separate vendor bills.
• Clean, high-quality data without unnecessary volume: Atatus helps teams reduce noisy logs, avoid excessive trace sampling, and store only the necessary data. The outcome is significantly lower storage costs.
• Straightforward, predictable pricing: You pay based on usage and team size, not unpredictable ingest-based or retention-based models. This keeps budgets stable even when systems grow.
• Faster debugging saves engineering hours: Clear traces, detailed logs, and real-time metrics help teams resolve issues quicker. That saves countless hours your team would otherwise spend diagnosing under pressure.
• Performance insights reduce compute and DB spend: Atatus makes it easy to identify slow endpoints, heavy queries, and resource-hungry services. Fixing these reduces cloud usage and helps you run leaner infrastructure.

By adopting Atatus, teams get full visibility across their stack while reducing the expenses that come with both cloud usage and multiple monitoring tools.

Conclusion

Observability has moved far beyond error tracking and dashboards. It’s now one of the most effective ways for engineering teams to reduce infrastructure, storage, operations, and downtime costs. With the right level of visibility, you eliminate inefficiencies, remove redundant tools, strengthen performance, and run a more predictable and affordable tech stack.

Atatus helps teams achieve this by combining complete observability with a pricing model built to keep costs in control. Whether your goal is to shrink log storage, reduce cloud waste, speed up debugging, or consolidate tools, Atatus gives you the visibility and efficiency to operate at scale without overspending.

Frequently Asked Questions

1) How much cost can observability actually save?

Engineering teams commonly save 20–60% across tooling, storage and operational workload. The highest immediate returns typically come from consolidating tools and optimizing log retention.

2) What types of storage cost savings are realistic?

Storage savings usually fall between 25–50% when teams remove noisy logs, tighten retention and archive rarely used data. Large environments can save even more.

3) How do I measure MTTR improvements?

Record detection-to-recovery time and compare it monthly. Track:

• Reduction in escalations
• Reduction in repeated incidents
• Shorter investigation timelines

A 30% MTTR improvement is considered meaningful.

4) Is observability worth the upfront investment?

Yes, provided you focus on quick wins like storage optimization and tool consolidation. These alone often cover the cost of the platform within months.

5) Do I need to overhaul my stack to adopt observability?

Not at all. Start with the highest-cost or highest-impact services and expand gradually.