Compare the Top Debugging Tools that integrate with AWS Lambda as of June 2025

This a list of Debugging tools that integrate with AWS Lambda. Use the filters on the left to add additional filters for products that have integrations with AWS Lambda. View the products that work with AWS Lambda in the table below.

What are Debugging Tools for AWS Lambda?

Debugging tools, also known as debuggers, are software tools that enable web developers and software developers to debug their code and applications in order to improve the quality and security of the code and application. Compare and read user reviews of the best Debugging tools for AWS Lambda currently available using the table below. This list is updated regularly.

  • 1
    AWS X-Ray
    AWS X-Ray helps developers analyze and debug production, distributed applications, such as those built using a microservices architecture. With X-Ray, you can understand how your application and its underlying services are performing to identify and troubleshoot the root cause of performance issues and errors. X-Ray provides an end-to-end view of requests as they travel through your application, and shows a map of your application’s underlying components. You can use X-Ray to analyze both applications in development and in production, from simple three-tier applications to complex microservices applications consisting of thousands of services.
  • 2
    Amazon SageMaker Debugger
    Optimize ML models by capturing training metrics in real-time and sending alerts when anomalies are detected. Automatically stop training processes when the desired accuracy is achieved to reduce the time and cost of training ML models. Automatically profile and monitor system resource utilization and send alerts when resource bottlenecks are identified to continuously improve resource utilization. Amazon SageMaker Debugger can reduce troubleshooting during training from days to minutes by automatically detecting and alerting you to remediate common training errors such as gradient values becoming too large or too small. Alerts can be viewed in Amazon SageMaker Studio or configured through Amazon CloudWatch. Additionally, the SageMaker Debugger SDK enables you to automatically detect new classes of model-specific errors such as data sampling, hyperparameter values, and out-of-bound values.
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