FastAPI vs Flask

Flask and FastAPI look alike at first glance. Both are small, both let you write a route as a decorated function, and neither gives you an admin or an ORM the way Django does. The difference is what each one assumes you are building.

Flask assumes a web application. It ships with Jinja templates, sessions, and a request context, and it runs on WSGI, the synchronous Python server interface that most of the Python web has used for twenty years. Everything else, from forms to database access to API validation, comes from extensions you pick.

FastAPI assumes an API. It runs on ASGI, so async is the default, and it uses your Python type hints to validate every request, serialize every response, and generate an OpenAPI schema with interactive docs. You write less glue code for an API, and the contract between backend and clients is checked on every call.

Pick FastAPI when the backend's main job is answering clients in JSON: a mobile app, a React or Next.js frontend, a partner integration. Pydantic models on each endpoint mean bad input is rejected with a clear error before your code runs, and the frontend team can generate a typed client from the schema.

Pick it when requests wait on something slow. A language model call, a streamed answer over server-sent events, a WebSocket held open for a dashboard, or three third-party APIs called in parallel all fit an async framework. In Flask, each of those waits ties up a worker, and you scale by adding workers.

And pick it when you are starting fresh and the team is comfortable with type hints. The typing that FastAPI asks for is the same typing that makes a codebase easier to change in a year.

Keep Flask when you have a Flask app that works. A rewrite to FastAPI buys you typed endpoints and async, and costs you weeks of moving routes, extensions, and tests across. If one part needs async, such as a streaming AI feature, a small FastAPI service beside the Flask app is usually the cheaper move.

Pick Flask when the product is a small server-rendered site or an internal tool with a handful of pages and forms. Jinja templates, sessions, and Flask-WTF cover that well, and FastAPI's strengths in typed JSON APIs do not help much there.

And Flask's age is a real advantage. Its extensions are mature, almost every problem has a written answer, and a Python developer who has never seen your codebase has very likely worked in Flask before.

Flask has supported async views since version 2.0, which surprises people. The catch is that Flask still runs on WSGI, so an async view runs its event loop inside a worker that is held for the whole request. You can await several calls inside one request, but you do not get the cheap, long-lived connections that ASGI gives you. The Pallets project maintains Quart for teams that want Flask's API on ASGI.

FastAPI's async does not make slow code fast. A blocking database driver or a CPU-heavy function inside an async endpoint stalls the event loop for every request, which is worse than Flask's behaviour. The rule is the same in both: blocking work goes to a thread pool or a background worker, and long jobs go to a queue.

Where async genuinely changes the picture is concurrency: many open connections that are mostly waiting. Streaming chat, live dashboards, and gateways in front of slower services all fit it.

Every Python API on our work page runs on FastAPI, except SigmaCPM, which uses Django REST Framework. We have not shipped a Flask application to production, so we cannot tell you what a Flask app is like in its third year from our own experience. What we can tell you is why we chose FastAPI each time, and those reasons are below.

If you have a Flask codebase and need help, we will read it as Python, with the same habits we bring to any inherited app: run it, test it, write down what we find, and change as little as the ticket needs.