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FastAPI Architecture — The Starlette + Pydantic + Uvicorn Stack

The reality behind FastAPI's "fast" claim

When asked why FastAPI is fast, most say "async support." True, but that's only half.

3-Layer Structure

Uvicorn — ASGI server. uvloop (libuv-based) + httptools (C parser) for HTTP parsing. 2-3x faster than pure Python. This is a big part of "fast."

Starlette — ASGI framework. Routing, middleware, WebSocket, static files. FastAPI inherits Starlette. FastAPI() is a Starlette subclass.

Pydantic — Data validation. V2 core written in Rust (pydantic-core). JSON parsing + validation runs at C/Rust speed.

FastAPI's own code is glue connecting these three layers via type hints. @app.get registers Starlette routes while reading function signature type hints to auto-generate Pydantic validation.

Dev Speed Is Also Part of "Fast"

Just write type hints and you auto-get: request parsing, validation, OpenAPI docs, JSON serialization. Manual implementation would be 3-5x more code.

Key Points

1

Uvicorn (ASGI server) parses HTTP at C level

2

Starlette handles routing/middleware

3

FastAPI reads type hints to auto-generate Pydantic validation

4

Response also auto-serialized: Pydantic model → JSON

Use Cases

REST APIs — auto docs + validation from type hints alone ML model serving — concurrent inference requests via async

References

Related

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FastAPI Depends() — Dependency Injection via Function Signatures
Injecting DB sessions, auth, pagination as function parameters
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← 🏷️ Type Hints — They Do Nothing at Runtime 💉 FastAPI Depends() — Dependency Injection via Function Signatures →