Python memory usage

Use Datadog to track and optimize memory usage for your Python applications.

Easily correlated information on your Python memory usage, allocations, and management helps speed up the resolution of disruptions and slow downs in end user experience. Optimize Python memory in context with Datadog tools like APM and the Continuous Profiler.

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

Track Python memory usage in context.

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

Optimize Python memory usage and other performance metrics with Datadog APM.

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

Python memory usage can be monitored with the Continuous Profiler.

Get full visibility into Python memory usage

Correlate and alert on Python memory data from multiple sources in one platform.

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Datadog brings together end-to-end traces, metrics, and logs to make your applications, infrastructure, and third-party services entirely observable.

Platform Diagram

Next-generation Python memory usage monitoring

Track and analyze Python application performance in context.

Datadog Watchdog

Auto-detect Python performance issues without manual alert configuration.

App Analytics

Find, filter, and analyze Python stack traces with a few clicks.

Service Map

Monitor Python application interdependencies in real time.

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Python Memory Usage Monitoring Resources

Learn best practices for monitoring Python memory usage and more.

Datadog Starter Kit

Datadog Python Documentation

Monitoring Modern Infrastructure eBook

Technology Integrations

Python Monitoring with Datadog

How to collect, customize, and centralize Python logs

Tracing asynchronous Python code with Datadog APM

Instrument your Python applications with Datadog and OpenTelemetry

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