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 category breakdowns such as code cache, class loading, and heap size

Spend Less Time Troubleshooting

  • Rapidly analyze call stacks by any key attribute such as method, thread, and package with a point-and-click interface
  • Speed up MTTD and MTRR; search profiles using simple tags and facets – no complex queries
  • Pinpoint CPU, memory, and latency bottlenecks in seconds and break profiles down by method, class, or line number


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
  • Get actionable insights; easily identify and address performance issues such as blocked threads, inefficient garbage collection, and memory leaks with an automatic heuristic analysis and a summary of the main problem areas in your code
  • Avoid outages or minimize their impact with machine-learning-based alerts for instant anomaly or outlier detection

Reduce Cloud Provider Costs

  • Easily identify the most resource-consuming methods or classes in your application and improve cpu utilization, memory allocation, and user latency
  • Discover underutilized cloud servers and on-premise servers via the real-time, auto-generated host map
  • Reduce excess capacity based on high-granularity historical data to save on operational costs

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