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What Is FastAPI? Python's Modern API Framework, Explained

FastAPI is a Python framework for building APIs that uses type hints for validation and generates interactive documentation automatically. What it is, a minimal example, Pydantic models, async support, automatic docs, how it compares with Flask and Django, and why it's popular for AI backends.

FastAPI is a Python framework for building APIs — the back-end endpoints your front-end, mobile app or other services call. It's become one of the most popular Python frameworks, especially for AI and machine-learning back-ends. (What is an API?)

A minimal example

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Order(BaseModel):
    product_id: int
    quantity: int = 1

@app.get("/health")
def health():
    return {"ok": True}

@app.post("/orders")
def create_order(order: Order):
    return {"product_id": order.product_id, "quantity": order.quantity}

Run it:

pip install "fastapi[standard]"
fastapi dev main.py

Open http://127.0.0.1:8000/docs — there's already interactive documentation for your API.

What makes it different

Type hints do the work

You describe your data with normal Python type hints and Pydantic models. FastAPI uses them to:

  • validate incoming data — send quantity: "lots" and you get a clear 422 error, not a crash,
  • convert types — the string "5" in a query parameter becomes the integer 5,
  • document the API automatically,
  • give your editor autocomplete.

One definition, four benefits. (Form validation explained)

Automatic documentation

Every FastAPI app gets interactive docs at /docs (Swagger UI) and /redoc, generated from an OpenAPI description of your endpoints. Front-end developers — and AI tools — can see exactly what each endpoint expects. (OpenAPI vs Swagger)

Async support

Endpoints can be async def, so one server process can handle many requests that are waiting on databases, other APIs or AI models at the same time. Useful for streaming AI responses. (Async/await explained, Streaming LLM responses)

Dependency injection

A clean way to share things like database sessions or "the current logged-in user" across endpoints:

@app.get("/me")
def me(user: User = Depends(get_current_user)):
    return user

FastAPI vs Flask vs Django

FastAPI Flask Django
Best for APIs Small apps and APIs Full websites with admin
Validation Built in (Pydantic) Add a library Forms / DRF serializers
Auto API docs Yes No With extras
Async First-class Partial Partial
Database / ORM Bring your own (SQLAlchemy, SQLModel) Bring your own Built in
Admin panel No No Yes

(Flask vs Django vs FastAPI, What is Django?)

Most AI and data tooling is in Python. FastAPI makes it quick to wrap a model, an LLM call or a data pipeline in a clean, documented, validated API that a React or mobile front-end can use. (How to build an AI app)

What it doesn't include

FastAPI is focused on the API layer. You add:

Running it in production

fastapi dev is for development. In production use fastapi run or Uvicorn (often with several workers) behind a reverse proxy with HTTPS. (Deploy a FastAPI app to production)

EasySpawn runs your FastAPI app behind HTTPS with Postgres and background workers on the same server, with Claude Code ready to add endpoints and tests. See how it works or join the waitlist.

Related: Flask vs Django vs FastAPI · Deploy a FastAPI App to Production · Designing a REST API · What Is Python?

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