FastAPI vs Flask, side by side

For a new JSON API in 2026, pick FastAPI: it validates requests from your type hints, supports async natively and writes interactive docs for you. Pick Flask when you want server-rendered HTML pages with Jinja templates, depend on a specific Flask extension, or maintain an existing Flask codebase. Both are mature and small, so the real difference is what you get out of the box.

Versions on PyPI as of 25 October 2026: fastapi 0.162.0 (Python 3.10 or newer) and Flask 3.1.3 (released 19 February 2026, Python 3.9 or newer). There is no requests-per-second chart here, because we have not run our own benchmark and borrowed numbers rarely match a real app.

Quick verdict

• Building an API for a React, mobile or other frontend: FastAPI.
• Building a classic website with server-rendered pages and forms: Flask.
• You want request validation and API docs without extra packages: FastAPI.
• You need a particular Flask extension your team already trusts: Flask.
• Lots of slow network calls per request, such as calling AI or payment APIs: FastAPI's async support helps.
• Learning web development for the first time: either works. Flask has fewer concepts on day one; FastAPI catches more mistakes for you.

The same endpoint in both

Here is an endpoint that accepts a new user and returns it, with basic validation.

FastAPI:

from fastapi import FastAPI
from pydantic import BaseModel, Field

app = FastAPI()


class UserIn(BaseModel):
    name: str = Field(min_length=1)
    age: int = Field(ge=13)


@app.post("/users", status_code=201)
async def create_user(user: UserIn):
    return {"name": user.name, "age": user.age}

Flask:

from flask import Flask, jsonify, request

app = Flask(__name__)


@app.post("/users")
def create_user():
    data = request.get_json(silent=True) or {}
    name = data.get("name")
    age = data.get("age")
    if not isinstance(name, str) or not name:
        return jsonify(error="name is required"), 400
    if not isinstance(age, int) or age < 13:
        return jsonify(error="age must be a number of at least 13"), 400
    return jsonify(name=name, age=age), 201

The Flask version is perfectly readable, but the checks are hand-written. In FastAPI, the model is the check, and wrong input returns a 422 with a message naming the field. Flask projects usually add a library such as Marshmallow or Pydantic to get the same effect.

Performance and async

FastAPI is built on Starlette and the ASGI standard, and endpoints can be async def. That means one server process can wait on many slow network calls at once, such as database queries, external APIs or AI model calls, without tying up a thread for each.

Flask is built on WSGI. Since Flask 2.0 you can write async views, but each request still runs in a worker, so you do not get the same concurrency gains. For many apps this does not matter: a Flask app behind Gunicorn with a few workers handles plenty of traffic.

Where speed differences show up is high concurrency with lots of waiting on I/O. For CPU-heavy work, neither framework is the bottleneck; your own code is. Public benchmark charts vary a lot by setup, so test your own workload if raw throughput is the deciding factor.

Validation and typing

FastAPI makes Python type hints do real work.

• Request bodies, path and query parameters are validated and converted automatically.
• Response models can filter what you send back, so you do not leak fields such as password hashes.
• Editors give accurate autocomplete because everything is typed.
• Interactive docs at /docs and /redoc, plus an OpenAPI schema, are generated from the same types.

Flask leaves these choices to you. That is freedom if you want a particular style, and extra work if you just want it done.

Ecosystem and extensions

Flask has been around since 2010, and its ecosystem shows it: Flask-Login for sessions, Flask-WTF for forms, Flask-SQLAlchemy, Flask-Migrate, Flask-Admin and many more. Tutorials, Stack Overflow answers and existing company code are plentiful.

FastAPI is younger, first released in 2018, but it has grown quickly. Instead of framework-specific extensions, it tends to use general Python libraries directly: SQLAlchemy or SQLModel for databases, Alembic for migrations, and Pydantic for data. Authentication helpers for OAuth2 and JWT are covered in the official docs.

For server-rendered HTML, Flask's Jinja integration is still the more natural fit. FastAPI can render Jinja templates too, but most FastAPI projects pair it with a separate frontend.

Learning curve

Flask's first-day surface is tiny: an app object, a route decorator, a request object. You can explain it in five minutes.

FastAPI adds a few ideas early: type hints, Pydantic models, dependency injection and the difference between async def and def. Those take a bit longer to learn, but they pay you back by catching mistakes before they reach users. If you already write typed Python, FastAPI will feel natural.

Testing and deployment

Both frameworks are easy to test. Flask ships a test client on the app object, so app.test_client() lets you call routes without a server. FastAPI offers TestClient, built on HTTPX, which works the same way, and because inputs are typed, many bad-input tests come down to checking for a 422.

Deployment differs slightly. A Flask app usually runs behind a WSGI server such as Gunicorn or Waitress, often with Nginx in front. A FastAPI app runs on an ASGI server such as Uvicorn, sometimes managed by Gunicorn with Uvicorn workers, and the fastapi run command starts a production server for you. Both fit in a small Docker image and run on any host that runs Python.

One practical tip for either: serve your built frontend from the same address as the API, or put both behind one proxy. You avoid CORS headaches, and cookies for login behave the way you expect. That is how VibeDream projects are laid out: the FastAPI app serves the built React files and the /api routes from one place, and Put it online publishes the whole thing with HTTPS.

Which one AI app builders pick and why

AI app builders and coding agents increasingly default to FastAPI for Python backends, and VibeDream is one of them: every app it builds is React + FastAPI. The reasons are practical.

• Types make generated code easier to check. When an AI adds a field, the model and the docs change together.
• Errors are specific. A 422 that names the field is something an AI can read and fix.
• Async fits modern apps, which call outside APIs all the time.
• A clean JSON API pairs naturally with a React frontend.

If you build an app on VibeDream, you can open the backend folder in the Code tab, download the project as a ZIP or sync it to GitHub, and run it locally with fastapi dev.

Moving from Flask to FastAPI

You do not have to rewrite everything at once. Common steps:

• Start new endpoints in FastAPI and leave old Flask routes running behind the same proxy until you migrate them.
• Turn your request checks into Pydantic models first; they work in both frameworks.
• Replace Flask-SQLAlchemy with plain SQLAlchemy or SQLModel.
• Replace Flask-Login sessions with FastAPI dependencies that read a cookie or token.
• Add tests with FastAPI's TestClient as you go.