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How FastAPI Works

Understand FastAPI from the ground up — from routing to lifecycle and real-world usage

Niklas L.
7 min read

How FastAPI Works 🚀

From routing and lifecycle to real-world usage — a practical guide for curious developers By Niklas L.

Let’s be honest, building APIs in Python used to feel more tedious than it needed to be. Between repetitive validation, manual docs, and parsing inputs by hand, most frameworks had you doing the same grunt work over and over again.

Then FastAPI showed up and changed that completely.

In this post, we’ll explore what makes FastAPI different, how it works behind the scenes, and how you can get the most out of it. Whether you’re just discovering it or already building APIs with it, this guide will help connect the dots.

What is FastAPI?

FastAPI is a modern Python web framework built for high performance and developer productivity. It’s designed to help you write clean, reliable APIs quickly, with as little boilerplate as possible.

Under the hood, it’s powered by two excellent libraries:

The magic of FastAPI is that it uses Python’s type annotations not just for better editor support, but as the foundation for validating inputs, generating docs, and keeping your code clean.

If you’ve ever written a Flask app and wished for automatic validation or built-in docs, FastAPI will feel like a breath of fresh air.

What’s Really Happening: FastAPI is Starlette With Extras

When you initialize a FastAPI app like this:

from fastapi import FastAPI

app = FastAPI()

You’re actually creating a Starlette app that’s been extended with a few really useful features.

Here’s what FastAPI adds:

So while Starlette gives you the barebones async web toolkit, FastAPI layers on developer-friendly tools that make building APIs smoother and faster.

What Happens When a Request Comes In?

Understanding FastAPI’s request lifecycle helps when you’re debugging or trying to reason about performance.

Here’s the general flow of a request:

Client Request
  ↓
FastAPI App
  ↓
Middleware
  ↓
Route Matching
  ↓
Dependency Injection
  ↓
Input Validation
  ↓
Your Endpoint
  ↓
Response Serialization
  ↓
Client Response

Let’s break it down:

  1. The request hits your FastAPI app, which is actually a Starlette ASGI application.
  2. Any installed middleware runs first. This includes things like logging, error handlers, or CORS.
  3. The router checks the path and method, then finds the matching route handler.
  4. FastAPI resolves any dependencies you’ve defined using Depends.
  5. Input data is parsed and validated automatically using Pydantic.
  6. Your endpoint function runs with validated inputs.
  7. Whatever you return gets serialized into the appropriate response format (usually JSON).
  8. The response is sent back to the client.

It’s a clean, layered process that hides a lot of complexity without limiting your flexibility.

How to Define Routes

FastAPI supports two main patterns for defining routes.

1. Inline Routes on the App

Good for small projects or quick demos:

@app.get("/items/{item_id}")
def read_item(item_id: int):
    return {"item_id": item_id}

FastAPI uses the type hint (int) to automatically validate the input and convert it for you.

2. Modular Routing with APIRouter

For bigger apps, this helps you stay organized:

from fastapi import APIRouter

router = APIRouter(prefix="/users", tags=["users"])

@router.get("/{user_id}")
def get_user(user_id: int):
    return {"user_id": user_id}

app.include_router(router)

With routers, you can group related endpoints, apply route prefixes, and keep your code modular.

What Happens Behind the Scenes When a Route Runs?

Once a request is matched to an endpoint, here’s what FastAPI does internally:

  1. Starlette identifies the correct route and sets up an APIRoute instance.
  2. FastAPI wraps the endpoint function using get_route_handler() and resolves dependencies.
  3. It parses and validates request data using Pydantic or basic types.
  4. Your function runs with clean, validated arguments.
  5. The return value is serialized into a response object.

It feels like calling a function with typed parameters, but behind the scenes, FastAPI is doing a lot of work to ensure everything is safe and correct.

Dependency Injection: Clean and Reusable Logic

FastAPI includes a lightweight but powerful dependency injection system that lets you plug in logic like database sessions, authentication, or configuration anywhere you need it.

Here’s an example:

from fastapi import Depends

def get_db():
    db = create_db_session()
    try:
        yield db
    finally:
        db.close()

@app.get("/items/")
def read_items(db=Depends(get_db)):
    return db.query(Item).all()

Using Depends, FastAPI takes care of running get_db, handling the generator lifecycle, and injecting the result. This makes testing, reusing, and organizing your code a lot easier.

Async is Built In

Unlike some older frameworks that added async as an afterthought, FastAPI was designed with async and await from day one.

@app.get("/async-data")
async def get_data():
    result = await fetch_data()
    return result

This means you can handle thousands of concurrent requests efficiently, whether you're talking to a database, hitting external APIs, or working with I/O-heavy operations.

Quick Example: A Simple CRUD API

Here’s a fast example that shows how little code you need to build something real:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Item(BaseModel):
    name: str
    description: str = None
    price: float
    tax: float = None

@app.post("/items/")
async def create_item(item: Item):
    total = item.price + (item.tax or 0)
    return {"name": item.name, "total_price": total}

@app.get("/")
def read_root():
    return {"message": "FastAPI is working!"}

To run it:

pip install fastapi uvicorn
uvicorn main:app --reload

Check out http://127.0.0.1:8000/docs for the built-in Swagger UI.

Boosting Performance: Use ORJSON

If your API returns a lot of data, you can switch to ORJSON for faster serialization:

from fastapi import FastAPI
from fastapi.responses import ORJSONResponse

app = FastAPI(default_response_class=ORJSONResponse)

It’s a quick way to improve performance, especially in high-load environments.

Frequently Asked Questions

How is FastAPI different from Flask or Django REST Framework? Flask is minimal and synchronous. DRF is powerful but heavyweight. FastAPI is async-first, with automatic docs and validation using type hints.

Does FastAPI support WebSockets? Yes. WebSocket support comes from Starlette, so you can use it just like any other route.

What’s the best way to handle authentication? FastAPI supports OAuth2, JWT tokens, API keys, and more through its dependency system.

Can I use FastAPI for microservices or serverless? Yes. It’s small, fast, and async, which makes it a great fit for microservices or serverless functions using Mangum or other adapters.

Can I use FastAPI with GraphQL? Yes, using libraries like Ariadne or Strawberry. FastAPI is REST-first, but it plays nicely with GraphQL integrations.

Can I run background tasks? Definitely. Use BackgroundTasks for lightweight jobs or plug in Celery or APScheduler for more complex workflows.

Wrapping Up

FastAPI is one of the most exciting things to happen to Python web development in years. It combines the raw speed of Starlette with the data modeling power of Pydantic, and wraps it all in a clean, modern, async-first design.

If you're building an API today — whether it’s for microservices, data pipelines, or backend systems — FastAPI is worth your time. It’s fast, productive, and feels like it was built by someone who actually writes APIs for a living.

Resources to Explore:

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