Python Memory Usage

Track Python memory usage in context

Ensure the uptime and performance of your Python applications with unified and customizable performance views.

Set alerts to quickly identify memory usage errors and remedy them by manually allocating or releasing memory
Rapidly analyze Python memory issues by correlating runtime metrics, garbage collection logs, and code profiles
Visualize and investigate Python memory usage metrics alongside performance data from all of your systems in one place

Optimize Python performance with Datadog APM

Understand your Python memory usage with end-to-end distributed tracing and APM.

Monitor Python memory usage and other performance metrics through code deployments and evaluate them over time
Pinpoint bottlenecks and errors in your Python applications by tracing requests across distributed systems
Use the Datadog Continuous Profiler to inspect methods, classes, and threads related to Python memory usage issues

Monitor Python memory usage with the Continuous Profiler

Use always on profiling for each line of your Python code to evaluate service performance and user experience in real time.

Speed up MTTR by automatically identifying Python memory usage issues like inefficient garbage collection or memory leaks
Accelerate the root cause analysis of slow requests with a breakdown of time spent by method on garbage collection, synchronization, and I/O
Seamlessly aggregate memory usage profiles across hosts, services, and code versions