# Python Error Monitoring & Performance Monitoring > Diagnose failures in Django, Flask, FastAPI, and Celery with local variables in the stack, function-level profiling, and logs. Free to start. **URL:** https://sentry.io/for/python/ --- Python error monitoring with actionable insight into every exception. See the full picture behind any Python error, resolve performance bottlenecks, and get framework-specific coverage that spans Django application monitoring, Flask, FastAPI, and Celery. **Documentation:** [https://docs.sentry.io/platforms/python/](https://docs.sentry.io/platforms/python/) ## Features ### Python Performance Monitoring Within minutes after installing Sentry, software teams can trace Python [performance](/product/tracing/) issues back to a poor-performing API call and surface every related code error. From Django application monitoring to FastAPI performance, engineering managers and developers get a single tool to optimize their code and deliver fast customer experiences. ### Complete Stack Traces, With Local Variables See local variables in the stack for prod errors, just like in your dev environment. Introspect more deeply into the runtime and jump into the frame to get additional data for any local variable. Filter and group Python exceptions intuitively to eliminate noise, whether you're doing Flask error handling, tracking Django views, or monitoring Celery jobs. ### Fill In the Blanks Expose the important events that led to each Python exception: SQL queries, debug logs, network requests, past errors. Improve debugging workflow with a full view of releases so you can mark errors as resolved and prioritize live issues. ### Profiling, Down to the Function Profiling lets you see what parts of your code are consuming the most resources, like CPU or memory, in your application— so you can optimize them before end user experience is impacted. Test your application performance in any environment, including in production, without writing manual tests or extensive troubleshooting. ### Your Logs, Next to the Trace Ship your Python logging output to Sentry and query it next to the [trace](/product/tracing/) that produced it. The `logging` calls you already write become searchable context on the exact request that failed, so retries, third-party API failures, and internal state are visible without a second tool. Learn more about [Logs in Sentry](/product/logs/). ## FAQ **What is the difference between Sentry and traditional logging?** Traditional logging provides you with a trail of events. Some of those events are errors, but many times they're simply informational. Sentry is fundamentally different because we focus on exceptions, or in other words, we capture application crashes. We discuss in more detail [here](/vs/logging/) and on our [blog](https://blog.sentry.io/lets-get-ready-to-monitor/). **What languages does Sentry support?** Sentry supports every major language, framework, and library. You can browse each of them [here](/platforms/). **How much does Sentry cost?** You can get started for free. Pricing depends on the number of monthly events, transactions, and attachments that you send Sentry. For more details, visit our [pricing page](/pricing/). **How does Sentry impact the performance of my app?** Sentry doesn't impact a web site's performance. If you look at the configuration options for when you initialize Sentry in your code, you'll see there's nothing regarding minimizing its impact on your app's performance. This is because our team of SDK engineers already developed Sentry with this in mind. Sentry is a listener/handler for errors that asynchronously sends out the error/event to Sentry.io. This is non-blocking. The error/event only goes out if this is an error. Global handlers have almost no impact as well, as they are native APIs provided by the browsers. **Can I send logs to Sentry?** Yes. Sentry Logs lets you send, view, and query structured logs from your application alongside your errors and traces. Because logs sit next to the error and the trace that produced them, you get the granular context — retries, API failures, internal state — that spans and stack traces alone can miss, without switching tools. Learn more about [Logs in Sentry](/product/logs/). **What is Seer, Sentry's AI debugging agent?** Seer is Sentry's AI debugging agent. It reads the full context Sentry already has — the error, its stack trace, the surrounding trace, logs, and profiles — to find the root cause of an issue and propose a fix. Seer can open a pull request with that fix, hand the work to an external coding agent, or review your PRs before they ship. Learn more about [Seer](/product/seer/) and [Autofix](/product/seer/autofix/). --- *Source: [sentry.io/for/python/](https://sentry.io/for/python/)*