Python APM and Performance Monitoring

Monitor response times, throughput, and errors in your Python applications with code-level observability for unparalleled performance insights.

Free Sign Up. No Credit Card Required. Cancel Anytime.

Uncover and Resolve Performance Bottlenecks Instantly

  • Pinpoint slow database queries, memory leaks, and CPU bottlenecks with ease.
  • Monitor Python applications in real-time to capture precise performance data for lightning-fast operations.
  • Automatically detect and mitigate performance anomalies before they impact your end-users.
  • Visualize critical performance metrics through intuitive dashboards for streamlined monitoring.
Python Dashboard

Achieve Complete Visibility in Python Monitoring

  • Gain comprehensive visibility across your Python applications, from real-time request handling to server-side performance.
  • Trace every request in your distributed systems and optimize for faster performance across your Python-powered apps.
  • Stay ahead of issues with proactive alerts that prevent minor inefficiencies from turning into major problems.
  • Leverage detailed logs and traces for quick troubleshooting and fine-tuned performance monitoring in Python.
Python Session Traces

Proactively Prevent Downtime for High Availability

  • Monitor Python applications to minimize downtime and guarantee seamless performance.
  • Conduct rapid root cause analysis to resolve issues swiftly and boost system stability.
  • Set up custom alerts to detect and address potential downtime risks early.
  • Ensure maximum uptime by continuously monitoring critical performance metrics in Python.
Python Service Map

Boost User Satisfaction with Enhanced Python Performance

  • Improve user experience with faster load times and responsive interfaces.
  • Reduce latency and keep users engaged with the high-speed performance of Python.
  • Optimize resource consumption to ensure peak performance under heavy traffic and demanding loads.
  • Enhance reliability through ongoing monitoring and fine-tuned performance optimization.
Python Performance

Elevate Python Application Insights with Comprehensive APM

  • Monitor and analyze the performance of your Python runtime, focusing on function execution times and overall efficiency.
  • Keep a close watch on how external libraries and services interact with your Python application, identifying bottlenecks and optimizing their performance.
  • Gain insights into your application's memory usage and CPU performance, helping you pinpoint resource constraints effectively.
  • Analyze integrated database and HTTP requests to optimize their execution, improving overall application responsiveness and reducing latency.
Python Metrics

Start fixing issues impacting your users right now

Try it free. No credit card required. Instant set-up.

Awesome Support

Best APM Monitoring tool

"Atatus customer service is just amazing. I had before New Relic and Stackify and I can honestly say that Atatus compared to those two is the leader! Leader in pricing and user interface and ability to drill down to the problem."

— S Herman Kiefus, DevOps Admin, Compass

We've Got Your Stack Covered!

Boost Framework Performance

Boost Framework Performance

Gain insights into your performance, enhancing transaction flow and speeding up error resolution.

Trace Every Request Instantly

Trace Every Request Instantly

Visualize end-to-end traces across your stack, ensuring that you catch every error, performance issue, or bottleneck before it affects users.

Identify Slow Queries Instantly

Identify Slow Queries Instantly

Pinpoint and resolve slow database queries and eliminate performance bottlenecks impacting your application's responsiveness, leading to faster response times

Stay Alert to Vulnerabilities

Stay Alert to Vulnerabilities

Get alerted to potential library vulnerabilities, preventing security risks before they affect your customers or compliance.

Simplify Logs, Troubleshoot Faster

Simplify Logs, Troubleshoot Faster

Centralize all your logs in one place, and quickly identify the root cause of issues using advanced filtering, pattern detection, and log pipelines.

Custom Metrics That Matter

Custom Metrics That Matter

Set up and track custom metrics that align with your app's KPIs to ensure you're monitoring exactly what matters most for your success.

Quick Request Analysis Anytime

Quick Request Analysis Anytime

Explore request-level analysis, including stdout APM logs, to understand execution times, bottlenecks, and areas that need optimization.

Align APM with Server Metrics

Align APM with Server Metrics

Correlate your app’s APM metrics with server health to get a complete picture of your application’s performance and infrastructure dependencies.

Actionable Alerts

Actionable Alerts

Receive real-time alerts for app performance degradations and critical issues. Take immediate action to prevent downtime and optimize user experiences.

FAQ on Python APM and Performance Monitoring

How to fix poor Python performance?

To fix poor Python performance:

  1. Identify performance issues: Use Python APM tools to detect root causes of slowdowns, such as inefficient function execution, memory leaks, or high CPU usage.
  2. Optimize code: Refactor slow-performing code, optimize Python services, and manage resource usage for better performance.
  3. Tune application settings: Adjust Python configurations like caching, database connection settings, and request handling to enhance performance.
  4. Manage resources: Ensure efficient use of CPU, memory, and optimize background tasks to improve overall application efficiency.
  5. Monitor regularly: Continuously monitor Python applications to detect and resolve performance issues proactively.
What metrics are needed for Python performance monitoring?

Essential metrics for Python performance monitoring include:

  1. Response times: Measures how quickly your Python application responds to user requests.
  2. Function execution performance: Tracks the efficiency and execution times of functions within your Python application.
  3. Memory usage: Monitors memory allocation and usage in your Python application.
  4. External library performance: Observes the impact and efficiency of interactions with external libraries and services.
  5. Request and response performance: Measures the performance of integrated database queries and HTTP requests.
Can I set up alerts for performance issues in my Python application?

Yes, Atatus allows you to set up custom alerts for performance issues in your Python application. You can configure alerts based on specific metrics like response times, error rates, and memory usage, ensuring immediate notifications when performance degrades.

What are the benefits of Python APM?

The benefits of Python application performance monitoring (APM) include:

  1. Real-time insights: Receive immediate feedback on Python application performance.
  2. Quick issue resolution: Identify and resolve performance issues faster.
  3. Resource management: Optimize CPU, memory, and background tasks in your Python applications.
  4. Improved user experience: Ensure smooth and responsive operation of your Python applications.
Does Atatus support distributed tracing for Python applications?

Yes, Atatus supports distributed tracing for Python applications, allowing you to trace requests as they move through different services and components. This helps you understand the end-to-end performance of your application and identify issues in microservices or complex architectures.