Top 10 Serverless Monitoring Tools in 2025

Monitoring serverless applications is critical to ensure optimal performance, reduce errors, and maintain end-to-end observability. Choosing the right serverless monitoring tools can help track serverless performance metrics, cold starts, and distributed traces across cloud functions. Below, we explore the top 10 cloud-native and third-party serverless monitoring solutions, highlighting their features, pros, cons, and best use cases.

Top 10 Serverless Monitoring Tools

1. Atatus
2. AWS CloudWatch
3. Google Cloud Monitoring
4. Azure Monitor
5. Datadog
6. New Relic
7. Dynatrace
8. Thundra
9. Epsagon
10. Lumigo

1. Atatus

Atatus provides comprehensive serverless application monitoring for AWS Lambda, Azure Functions, and Google Cloud Functions. It delivers real-time insights into errors, latency, cold starts, and end-to-end traces. With easy integration, developers and DevOps teams can gain full observability without heavy setup. Atatus is designed for both cloud-native and hybrid serverless architectures.

Key Features:

• Real-time serverless error tracking and alerts
• Serverless dashboards and alerts for quick insights
• Cold start monitoring and function-level performance metrics
• Distributed tracing across multiple services

Pros:

• Easy setup with minimal configuration
• Full-stack serverless performance monitoring
• Supports multiple cloud platforms

Cons:

• Advanced analytics require a paid plan
• Limited on-premise monitoring

Migration Complexity:

Low, supports plug-and-play integration with serverless applications

Best Use Cases:

1. Real-time monitoring of AWS Lambda, Azure Functions, and Google Cloud Functions
2. Applications requiring end-to-end serverless monitoring and tracing

2. AWS CloudWatch

AWS CloudWatch is a native monitoring service for AWS Lambda and other serverless workloads. It tracks invocations, errors, execution duration, and resource usage. CloudWatch integrates seamlessly with AWS Logs, Alarms, and dashboards, making it essential for cloud-native serverless environments.

Key Features:

• Serverless logs monitoring and metrics collection
• Custom alarms and dashboards
• Integration with AWS X-Ray for distributed tracing
• Performance metrics and resource utilization tracking

Pros:

• Fully native and deeply integrated with AWS services
• No additional installation needed
• Highly scalable for large serverless deployments

Cons:

• Can be complex to configure advanced metrics
• Limited support for non-AWS serverless platforms

Migration Complexity:

Low for AWS users; native integration makes adoption straightforward

Best Use Cases:

1. Monitoring AWS Lambda serverless functions in production
2. Tracking serverless function performance metrics at scale

3. Google Cloud Monitoring

Google Cloud Monitoring is a comprehensive observability solution within Google Cloud Platform (GCP) designed to provide insights into the performance, uptime, and overall health of cloud-powered applications and infrastructure. It offers a unified platform for collecting, analyzing, and visualizing metrics, events, and metadata from various sources.

Key Features:

• Tracks cloud function monitoring and performance metrics
• Real-time alerts for errors and latency
• Integrated dashboards and logs visualization
• Distributed tracing with Cloud Trace

Pros:

• Seamless integration with Google Cloud services
• Supports multiple monitoring views (logs, metrics, traces)
• Scales automatically with serverless workloads

Cons:

• Limited to Google Cloud ecosystem
• Steeper learning curve for non-GCP users

Migration Complexity:

Moderate; best suited for GCP-native applications

Best Use Cases:

1. Monitoring Google Cloud Functions serverless applications
2. Real-time alerting and serverless error tracking in GCP

4. Azure Monitor

Azure Monitor is the core platform for serverless application monitoring in Azure, providing unified observability of your serverless resources like Azure Functions and Logic Apps through metrics, logs, and traces collected via OpenTelemetry. Key tools include Log Analytics for querying and analyzing log data, Azure Monitor Workspace for time-series metrics, and dashboards and workbooks for visualization.

Key Features:

• Tracks serverless performance metrics and cold starts
• Serverless dashboards and alerts via Application Insights
• Real-time serverless error tracking
• Distributed tracing and telemetry for Azure Functions

Pros:

• Fully integrated with Azure ecosystem
• Strong analytics with Application Insights
• Automatic scaling of monitoring data

Cons:

• Limited outside Azure environment
• May require additional configuration for complex architectures

Migration Complexity:

Low to moderate for Azure-native serverless applications

Best Use Cases:

1. Monitoring Azure Functions serverless workloads
2. Tracking performance and errors in enterprise Azure apps

Ready to simplify serverless monitoring? Atatus provides real-time insights, error tracking, and full visibility so your apps stay fast and reliable. Request a Demo

5. Datadog

Datadog provides comprehensive monitoring for serverless architectures across AWS, Azure, and Google Cloud. It combines logs, metrics, and traces to deliver full observability. It offers detailed insights into function performance, cold starts, and resource usage. With automated alerts and customizable dashboards, teams can ensure reliability and optimize costs.

Key Features:

• Cross-cloud serverless monitoring
• Real-time serverless dashboards and alerts
• Cold start and execution duration tracking
• Distributed tracing and error reporting

Pros:

• Multi-cloud support
• Easy integration with CI/CD pipelines
• Rich visualization for serverless application monitoring

Cons:

• Pricing can be high for large workloads
• Some features require advanced configuration

Migration Complexity:

Moderate; requires agent integration or API setup

Best Use Cases:

1. Monitoring complex multi-cloud serverless functions
2. Tracking serverless function performance metrics in distributed apps

6. New Relic

New Relic offers observability for serverless applications, providing deep insights into execution times, cold starts, and errors. Distributed tracing allows tracking across microservices. It integrates seamlessly with AWS Lambda and other major cloud providers for end-to-end visibility. With real-time alerts and dashboards, teams can proactively detect issues and optimize performance.

Key Features:

• Function-level serverless performance monitoring
• Cold start tracking and error alerts
• Distributed tracing across microservices
• Integrated dashboards and analytics

Pros:

• Strong analytics and alerting
• Multi-cloud support
• Easy integration with popular serverless frameworks

Cons:

• Paid plans required for full functionality
• Can be overwhelming for small teams

Migration Complexity:

Moderate, installation of agents or SDKs required

Best Use Cases:

1. End-to-end monitoring of serverless functions
2. Applications requiring distributed tracing and performance insights

7. Dynatrace

Dynatrace provides AI-powered monitoring for serverless workloads, tracking performance, dependencies, and root-cause analysis automatically. It supports AWS Lambda, Azure Functions, and Google Cloud Functions with full-stack observability. Dynatrace helps DevOps teams resolve issues faster by correlating metrics, logs, and traces in real time.

Key Features:

• AI-assisted serverless observability
• Distributed tracing and serverless error tracking
• Performance metrics and resource monitoring
• Automated root-cause analysis

Pros:

• AI-powered insights reduce manual troubleshooting
• Multi-cloud support
• Automated anomaly detection

Cons:

• High learning curve
• Premium pricing

Migration Complexity:

Moderate, integration with cloud provider needed

Best Use Cases:

1. Enterprise-level serverless application monitoring
2. Applications requiring automated root-cause detection

8. Thundra

Thundra is designed specifically for serverless monitoring and debugging across multiple cloud providers. It provides insights into performance, errors, and cold starts. It offers deep visibility into AWS Lambda, Azure Functions, and Google Cloud Functions with end-to-end tracing. Developers and DevOps teams can quickly identify bottlenecks, optimize execution time, and improve overall reliability.

Key Features:

• Serverless dashboards and alerts
• Cold start monitoring and serverless performance metrics
• Distributed tracing and function profiling
• Supports AWS Lambda, Azure Functions, and GCP

Pros:

• Serverless-first approach
• Supports multi-cloud architectures
• Easy error detection and debugging

Cons:

• Smaller ecosystem compared to Datadog or New Relic
• Limited third-party integrations

Migration Complexity:

Low, plug-and-play integration with serverless apps

Best Use Cases:

1. Debugging and monitoring serverless functions
2. Applications requiring detailed function-level metrics

9. Epsagon

Epsagon focuses on serverless observability and automatic distributed tracing for cloud-native applications. It provides real-time monitoring of AWS Lambda, containers, and microservices with minimal setup. Epsagon helps teams troubleshoot faster by visualizing dependencies, errors, and performance bottlenecks.

Key Features:

• Serverless error tracking and alerts
• Distributed tracing and visualization
• Metrics and logs monitoring
• Multi-cloud support

Pros:

• Automatic tracing reduces manual setup
• Supports AWS Lambda, Kubernetes, and microservices
• Intuitive dashboards

Cons:

• Limited advanced analytics
• Pricing scales with usage

Migration Complexity:

Moderate, requires SDK integration

Best Use Cases:

1. Observability for multi-cloud serverless applications
2. Detecting bottlenecks in serverless workflows

10. Lumigo

Lumigo offers end-to-end serverless monitoring and debugging with real-time tracing of serverless workflows. It automatically detects errors, timeouts, and cold starts to help teams resolve issues quickly. Lumigo also provides visual service maps, making it easier to track dependencies across microservices.

Key Features:

• Distributed tracing across functions
• Cold start and error monitoring
• Real-time alerts and dashboards
• Performance metrics tracking

Pros:

• Detailed serverless function performance monitoring
• Easy visualization of function workflows
• Multi-cloud support

Cons:

• May require time to learn full platform
• Premium features require paid plans

Migration Complexity:

Moderate, SDK and cloud account integration required

Best Use Cases:

1. Tracking complex serverless workflows
2. Debugging and monitoring multi-function applications

Conclusion

Serverless monitoring tools provide cross-cloud visibility, automated tracing, and in-depth insights into serverless workloads. These tools are ideal for teams running modern, event-driven applications and needing proactive performance, error, and dependency monitoring across multiple cloud environments.

Use serverless monitoring tools when:

• Your applications run on AWS Lambda, Azure Functions, or Google Cloud Functions.
• You need visibility into cold starts, execution times, and errors beyond what cloud-native tools provide.
• You want distributed tracing and end-to-end insights across multiple cloud providers and microservices.