FastAPI has rapidly become the #1 most-starred backend framework on GitHub, surpassing not only Python giants Flask and Django, but frameworks across other languages, including Gin, Laravel, Spring Boot, and Express.

Meet Sebastián Ramirez, the creator behind FastAPI.

The architecture behind FastAPI’s performance

FastAPI didn’t become one of the fastest-growing Python frameworks by accident. It’s built on a deliberate architectural foundation that solves real problems Python developers face daily.

The foundation: Starlette and Pydantic

FastAPI sits on top of two battle-tested libraries:

Starlette handles the web layer; routing, request handling, and async event loops. It’s the same ASGI framework that powers other high-performance Python web apps.

Pydantic manages data validation and serialization. Every piece of data entering your API gets validated against Python type hints automatically.

This combination delivers three things at once: fast I/O operations, strict type checking without manual effort, and built-in data validation that catches errors before they hit your business logic.

Async is the performance unlock

Here’s what separates a slow FastAPI app from a fast one: understanding when to use async def.

Use async for:

• Database calls
• External API requests
• File read/write operations
• Any I/O-bound task

A regular def blocks your entire application while waiting for a database response. An async def lets your server process other incoming requests during that wait time. That's where the performance gains come from.

The difference isn’t theoretical. On I/O-heavy workloads, properly async endpoints can handle 5–10x more concurrent requests than blocking ones.

Dependency injection: cleaner than it sounds

FastAPI’s Depends system is where the framework's design philosophy shines. Every request can automatically receive:

• A fresh database session
• Proper connection management
• Authentication verification
• Configuration values
• Cache clients

Your endpoint code stays focused on business logic. The infrastructure concerns live in reusable dependency functions.

async def get_current_user(token: str = Depends(oauth2_scheme)): # Auth logic lives here, not in every endpoint return user

@app.get("/profile") async def get_profile(user: User = Depends(get_current_user)): # This endpoint just handles the profile logic return user.profile

The benefit isn’t abstract. Endpoints become testable because you can swap dependencies. Code becomes maintainable because concerns stay separated.

Pydantic handles validation so you don’t have to

Define a BaseModel with your expected fields. FastAPI then:

• Validates incoming request data
• Serializes response data
• Returns clear error messages when validation fails

No more manual checks like if 'name' not in payload. No more hoping the frontend sent the right data types. The framework catches malformed requests before your code even runs.

class CreateUser(BaseModel): email: EmailStr age: int = Field(ge=18)

@app.post("/users") async def create_user(user: CreateUser): # If we get here, user.email is valid and user.age >= 18 return {"created": user.email}

Automatic validation isn’t a convenience feature; it’s a production safety net.

Auto-generated docs that stay current

FastAPI generates interactive documentation at /docs using your Python type hints. The Swagger UI appears automatically, always in sync with your actual endpoints.

Why this matters in practice:

• Frontend teams test endpoints without asking backend developers
• New team members understand the API immediately
• Documentation never drifts from the code

This saves hours of manual documentation work. More importantly, it keeps contracts between teams in sync without meetings or manual updates.

Background tasks for responsive APIs

Some operations don’t need to block the response. Use BackgroundTasks to offload:

• Email sending
• Log processing
• Webhook notifications
• Heavy computations

The pattern is simple: return a response to the user immediately, then process the slow task in the background. Users don’t wait for operations that don’t affect their immediate result.

@app.post("/signup") async def signup(email: str, background_tasks: BackgroundTasks): background_tasks.add_task(send_welcome_email, email) return {"status": "Account created"} # Email sends after the response is already on its way

FastAPI’s growth makes sense when you look at what it gets right: async performance by default, validation without boilerplate, dependency injection that scales, and documentation that maintains itself. These aren’t features, they’re solutions to problems every API developer encounters.

Resources

1. FastAPI
2. FastAPI Cloud
3. Pydantic

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The Rise and Rise of FastAPI was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.