Python Continuous Profiler | Datadog

Rapidly Optimize Python Performance with Continuous Profiling

Detect the most resource-consuming methods or classes in your Python applications in seconds with a lightweight, next-generation profiler that’s easy-to-use and always-on. Start your trial today, build a dashboard, and we’ll send you a free Datadog t-shirt!

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Product Benefits

Get Complete Visibility into Python Performance

  • Analyze 100% of your code in production including methods, classes, and threads across your entire stack
  • Seamlessly pivot between profiles and distributed traces with 1-click to get full context and identify the most resource-intensive requests
  • Get deeper insights into exceptions, garbage collection, I/O, locks, and packages with continuous observability and key category breakdowns such as code cache, class loading, and heap size

Spend Less Time Troubleshooting

  • Pinpoint CPU, memory, and latency bottlenecks in seconds and easily analyze profiles by method, class, or line number
  • Determine the root cause of slow requests with a breakdown of time spent by method on garbage collection, locks, and I/O
  • Speed up MTTD and MTRR; examine call stacks by any key attribute such as method, thread, and package with a point-and-click interface – no complex queries

Easily Optimize Both Legacy and Cloud Applications

  • Better maintain legacy and cloud apps with full visibility into code-level performance in dev, staging, and production environments
  • Continuously profile each line of code in any environment without affecting application performance and user experience
  • Quickly address issues with an automatic heuristic analysis and actionable summary of the main problem areas in your code; apply suggested fixes to improve application performance without having prior experience in code profiling

Reduce Cloud Provider Costs

  • Optimize resource consumption and save on compute costs with code profiling aggregations across hosts, services, and versions
  • Discover underutilized cloud servers and on-premise servers via the real-time, auto-generated host map
  • Further reduce unnecessary compute, storage or data transfer costs by combining performance metrics with critical cost data

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