Understanding APM and Distributed Tracing in the Observability Stack

To keep modern applications running smoothly, you need more than just basic monitoring. APM (Application Performance Monitoring) gives you a broad overview, tracking metrics like latency, errors, and system health. Distributed Tracing, on the other hand, shows the full journey of each request across services, helping you pinpoint the root cause of slowdowns or failures.

Together, APM and tracing give you the full picture of your application’s performance and help you fix issues faster when they arise. In this blog, we will break down what they are, how they differ, and how they work together in the observability stack.

In this Blog,

1. What does APM mean in Observability?
2. What is Distributed Tracing in observability?
3. APM vs Distributed Tracing: Key Differences Explained
4. How Distributed Tracing Enhances the APM Experience
5. When should you use APM or Distributed Tracing in Observability?
6. Getting Started with APM and Distributed Tracing Using Atatus
7. FAQs

What does APM mean in Observability?

Application Performance Monitoring (APM) is an essential part of the observability stack that helps track how well applications are performing in real time. It focuses on monitoring key metrics like response times, error rates, request throughput, and resource usage, giving a high-level view of the application's overall health.

The main goal of APM is to quickly detect performance issues, understand which parts of the system are affected, and improve the end-user experience. It allows engineering teams to monitor system behavior, set performance benchmarks, and respond to problems before they impact users.

Example:

Suppose a web application’s login page suddenly slows down. An APM tool can highlight increased response times, surface relevant error messages, and trace the issue back to a specific backend service or database query, enabling engineers to resolve it before it affects more users.

What is Distributed Tracing in observability?

Distributed Tracing in observability tracks the complete path of a request as it moves through different services in a distributed system. It offers detailed, request-level visibility showing how each service processes the request, how long each step takes, and where slowdowns or failures occur.

The main goal is to help developers identify performance bottlenecks and errors quickly by tracing the exact point of failure or latency. This makes troubleshooting faster and improves overall system reliability and performance.

Example:

In a microservices-based application, if a user request is slow, distributed tracing can reveal whether the delay is in the authentication service, database, or a downstream API helping teams identify the exact cause.

APM vs Distributed Tracing: Key Differences Explained

How Distributed Tracing Enhances the APM Experience

Distributed tracing significantly extends the capabilities of Application Performance Monitoring (APM) by adding deep, request-level visibility across distributed systems, especially in microservices environments. While APM provides high-level performance metrics like response times and error rates, distributed tracing captures how each request flows across services, revealing what happens at every step.

Here’s how distributed tracing complements and strengthens APM:

1. End-to-End Request Visibility

Distributed tracing follows a single request as it moves through different services, databases, queues, and APIs. This gives teams a complete, connected view of the entire request journey, something traditional APM might miss when monitoring services in isolation.

2. Bottleneck Detection

By mapping the full execution path of a request, tracing highlights where delays or errors occur, whether it's a slow database call, a failing microservice, or a misconfigured API. This helps identify performance bottlenecks with precision.

3. Faster Troubleshooting

Instead of jumping between dashboards or logs, IT teams can directly trace a failing request back to its source. This reduces mean time to resolution (MTTR) during outages or incidents and speeds up root cause analysis.

4. Performance Optimization

Distributed tracing shows where time is being spent across services, helping developers pinpoint inefficient code paths, network latency, or slow external dependencies. This enables focused, data-driven performance tuning.

5. Stronger Observability

Tracing adds contextual data to existing APM metrics by showing how services interact. When combined with logs and metrics, it completes the observability picture and allows deeper analysis of system behavior under real workloads.

6. Built for Microservices

In a microservices architecture, requests often pass through dozens of services. Distributed tracing is essential for understanding these interactions and identifying issues caused by inter-service communication or failures, something APM alone can't fully uncover.

When should you use APM or Distributed Tracing in Observability?

Understanding when to rely on APM and when to turn to distributed tracing depends on what kind of problem you are trying to solve. Both are essential tools in the observability stack, but they answer different questions and operate at different levels of detail.

Need a broad view of your application's performance? Use APM

If you want to monitor overall application health, track system-wide metrics, and be alerted to anomalies like increased error rates or slow response times, APM (Application Performance Monitoring) is your go-to solution.

It answers questions like:

• Is my app performing within acceptable thresholds?
• Are users experiencing high error rates?
• Which endpoints are slowing down?

Use APM for ongoing monitoring, SLA tracking, and performance trend analysis. It's especially useful in monolithic apps or when you are focusing on a single application’s performance.

Need to trace a specific request across multiple services? Use Distributed Tracing.

If you are working with microservices or serverless architectures, and you are trying to understand how a single request behaves across services, distributed tracing is the right tool.

It answers questions like:

• Why is this request taking so long?
• Which service is causing the delay or failure?
• What dependencies are involved in this transaction?

Use tracing when you are debugging complex performance issues, especially those involving multiple services, APIs, or infrastructure components.

Many IT teams use both: APM for day-to-day performance monitoring and threshold-based alerts, and distributed tracing for deep diagnostics and root cause analysis.

Getting Started with APM and Distributed Tracing Using Atatus

While tools like Jaeger offer distributed tracing, using separate tools for APM and tracing can lead to fragmented insights and slower issue resolution.

To debug efficiently, engineering and DevOps teams need a unified view of performance and request flow, all in one place. That’s exactly what Atatus provides.

Why choose Atatus?

Atatus offers an all-in-one observability platform with powerful features:

• APM and Distributed Tracing: Get complete visibility from metrics to traces, no context switching required.
• Real-Time Monitoring: Track response times, error rates, throughput, and slow transactions as they happen.
• OpenTelemetry Support: Easily ingest trace data from any instrumented application using OpenTelemetry SDKs.
• Service Maps and Trace Views: Visualize how requests move across services and identify bottlenecks instantly.
• Root Cause Analysis: Pinpoint errors and latency at the service or transaction level for faster debugging.
• Custom Dashboards & Alerts: Stay informed with real-time alerts and performance dashboards.

Ready to gain full visibility into your application? Get started with Atatus for free today.

FAQs

1. How does APM contribute to observability?

Application Performance Monitoring (APM) plays a central role in observability by providing real-time insights into application behavior, performance, and health. APM tools monitor key metrics (e.g., response times, throughput, error rates) and collect logs and traces, helping developers:

• Detect anomalies and bottlenecks
• Understand the user experience
• Drill down into slow transactions and errors
• Correlate backend performance with frontend behavior

By aggregating and analyzing telemetry data, APM enhances the observability pillars, logs, metrics, and traces; offering a holistic view of systems.

2. What is distributed tracing and how does it work?

Distributed tracing tracks requests as they traverse through multiple microservices in a distributed system. It works by:

• Assigning a trace ID to each request
• Logging spans (units of work) across services with metadata like start time, duration, and service name
• Collecting and visualizing the traces to show the entire request flow

This provides a visual map of how services interact, where delays occur, and which components are under stress.

3. Why is distributed tracing important for microservices?

In microservices architecture, a single user request might touch dozens of services. Without distributed tracing, it's hard to:

• Pinpoint performance issues across service boundaries
• Identify which service caused an error or delay
• Debug complex interactions and dependencies

Distributed tracing helps reduce MTTR (Mean Time to Resolution), ensures observability across services, and improves system reliability.

4. Can APM and tracing be used together?

Yes. In fact, APM and distributed tracing complement each other:

• APM gives a broad overview of application health and performance
• Distributed tracing provides granular, request-level insights

When integrated, they allow teams to correlate performance metrics with trace data, leading to faster and more accurate troubleshooting and optimization.

5. How does distributed tracing improve performance monitoring?

Distributed tracing improves performance monitoring by:

• Pinpointing latency sources across service boundaries
• Visualizing bottlenecks in the request flow
• Tracing the impact of code changes on response time
• Helping isolate performance regressions in specific services

6. What tools offer both APM and distributed tracing?

Several tools provide integrated APM and tracing capabilities, including:

1. Datadog – Unified observability with logs, metrics, traces, APM
2. Atatus – Full-stack observability, including APM and distributed tracing
3. Dynatrace – AI-powered APM with automatic distributed tracing
4. Elastic Observability – APM, logs, metrics, and traces on the Elastic Stack
5. OpenTelemetry + Grafana/Jaeger/Tempo – Open-source, modular observability stack

7. What are the challenges in implementing distributed tracing?

Implementing distributed tracing can be challenging due to:

• Instrumentation overhead – Manually adding trace logic or configuring SDKs
• Sampling strategy – Balancing detail with performance and cost
• Data volume – Managing large amounts of trace data
• Standardization – Ensuring trace context propagates across all services and languages
• Tooling integration – Choosing the right backend (e.g., Jaeger, Zipkin, Tempo)

Solutions like OpenTelemetry help reduce these complexities by offering standard APIs and SDKs.

8. How do you choose the right APM for observability?

When choosing an APM tool for observability, consider:

1. Data coverage: Support for metrics, logs, and traces
2. Microservices support: Strong distributed tracing capabilities
3. Integration: Compatibility with your stack (Kubernetes, cloud, frameworks)
4. Ease of use: Dashboards, alerting, and automated root cause detection
5. Scalability: Ability to handle high cardinality data
6. Cost efficiency: Pay-per-use or open-source options (like OpenTelemetry)
7. Community and support: Documentation, vendor backing, and plugin ecosystem