FastAPI vs Flask: Choosing a Python API Framework

FastAPI and Flask are both Python web frameworks, but they solve different problems. FastAPI is built for async APIs with built-in checks. Flask is a small toolkit that leaves those choices to you, per Codecademy's own guide.

For the first time, neither Django nor Flask is Python's most-used web framework. FastAPI took that spot, per a 2025 JetBrains survey covered below.

This guide compares the two on design, speed, and real fit. It also covers the part most guides skip. When is Flask still the right choice, not the old one?

What Each Framework Actually Is

Flask is a microframework. It gives you routing and a request/response cycle. The rest is your call: a database layer, an auth scheme, a template engine. That open design is the whole point.

FastAPI is narrower in purpose. It's built for typed APIs with checks and docs baked in. You declare a request shape with a Pydantic model. FastAPI checks every request against it. It rejects bad input before your code runs at all. That's per Leapcell's own write-up.

Both frameworks run on Python. Both use decorators for routes. That's where they stop looking alike. The real split sits one layer down, in how each one talks to the web server.

WSGI vs. ASGI: The Real Split

Flask runs on WSGI, the older synchronous interface. Every request claims one worker thread. That worker sits blocked until the request finishes. It stays blocked even while it's just waiting on a database call. (Better Stack).

FastAPI runs on ASGI, the async interface. A single worker can hold thousands of connections open at once. One request can wait on a database. The same worker just handles the next request instead of sitting idle.

That difference shows up hard under I/O-heavy load. One benchmark ran both frameworks with a single worker each. FastAPI served roughly 150 requests per second. Flask served about 19.

Under that same load, ASGI kept 100% of requests successful. WSGI failed on 76% of them. That gap isn't about raw CPU speed. It's about what happens to a worker while it waits.

What You Get for Free with FastAPI

FastAPI reads your Python type hints. It builds an OpenAPI schema from them on its own. Visit /docs on any FastAPI app. You get a working Swagger UI with zero extra code, as DEV Community's OpenAPI walkthrough shows.

A single Pydantic model class does three jobs at once. It checks incoming JSON against the shape you declared. It documents that shape in /docs, with no extra step. It gives your editor real autocomplete on every field. Flask needs a separate library, like Marshmallow, bolted on by hand, to do any of that.

Draftbase's own delivery API takes the same approach, even outside Python. It rejects bad shapes at the edge. It doesn't wait three functions deep into a handler to catch them. See how that API is designed for the full picture.

Where Flask Still Wins

Speed isn't the only axis that matters. Flask has one real advantage FastAPI doesn't: Jinja2, its built-in template engine. FastAPI has no first-class templating story of its own. (Contentful).

That matters for apps that render HTML pages, not just JSON. A dashboard needs a page with a form and a table, not an API contract. So does an admin panel, or a small internal tool. Flask does that in a few lines. FastAPI makes you reach for extra packages to get the same result.

Flask also wins on plain ease for small services. A synchronous internal tool under 50 endpoints, with no real concurrency need, gains nothing from ASGI. It just adds a new async mental model your team has to learn. (Netguru).

And Flask's ecosystem is older and wider. More Stack Overflow answers exist for it. More production war stories are already written down. That head start is worth something on a team without deep FastAPI experience yet.

Dependency Injection: A Feature Flask Skips

FastAPI ships a built-in dependency system, called Depends. You write a small function once, for a database session, a current user, an API key check. Then any route can ask for it by name, and FastAPI runs it before your route code fires.

Flask has no equivalent baked in. Most Flask apps reach for globals instead, like a database connection stored on flask.g, or a decorator that wraps each route by hand. That works. It just means every route repeats a bit of setup logic, or leans on shared state that's easy to get wrong under test.

The FastAPI version pays off most in testing. Swap one dependency for a fake version in a test file, and every route that uses it picks up the fake automatically. No monkeypatching, no global state to reset between tests. That's a small detail on paper. It saves real time once a project has more than a handful of routes that all need a database session.

Moving from Flask to FastAPI

Most teams don't pick one framework once and never look back. A common path starts with Flask for a quick internal tool, then hits a wall once real traffic or an ML model shows up.

A full rewrite is rarely the right first move. Both frameworks support the same core idea: a route, a handler function, a JSON reply. The routes themselves often port over close to line for line.

The real work is elsewhere. Old Flask routes lean on request.get_json() and manual field checks. Those checks turn into Pydantic models, one per route. That's the slow part. Skip a field, or get a type wrong, and FastAPI catches it at request time instead of three layers deep in your own code.

Deployment changes too. Flask apps run behind Gunicorn, a WSGI server. FastAPI apps need Uvicorn, or a similar ASGI server, in front of them. Swapping that piece is usually a config change, not new code. It's still worth testing under real load before you call the migration done.

The Underused Angle: Adoption Actually Flipped

Most guides stop at "FastAPI is faster, Flask is simpler." The real story is usage.

JetBrains surveyed over 30,000 working Python developers in 2025. FastAPI usage hit 38%. Django came in at 35%. Flask trailed at 34%.

FastAPI usage was only 29% in 2023. That's real growth in two years, not a rounding error.

The split runs along a rough timeline. Teams that built their Python API stack before 2020 mostly picked Flask. Teams that built after 2021 mostly picked FastAPI.

Machine learning work makes that split sharper. 42% of ML engineers use FastAPI now, against 28% on Flask. Async request handling matches how inference waits on GPU work. The fit isn't a coincidence.

Conclusion

FastAPI wins on raw throughput, built-in docs, and built-in checks. Flask wins on template rendering, simple setup, and a wider, older ecosystem. Neither claim is close to a toss-up anymore, once you look at the data.

Pick FastAPI for a new async, API-first service. That's the right call for real I/O concurrency, or ML inference behind it. Pick Flask for a small synchronous tool, or a server-rendered app. It also fits a codebase your team already knows well. See what an API call actually is for the shared basics. Or check Draftbase pricing for a CMS delivery API you can call from either one.