FastAPI

Why It Matters

FastAPI has become the default framework for AI application backends. Its async-first design handles the concurrent, I/O-heavy nature of LLM applications well, because while one request waits for OpenAI’s API response, others can be processed. This concurrency model significantly improves throughput compared to synchronous frameworks.

Type hints and Pydantic integration catch errors before they reach production. Define your request and response models with Python types, and FastAPI handles validation, serialization, and documentation automatically. When your LLM output needs to be structured JSON, Pydantic models enforce the schema at the API boundary.

For AI engineers, FastAPI’s combination of performance, developer experience, and production-readiness makes it the pragmatic choice. You can prototype quickly with hot reloading and auto-generated docs, then deploy the same code to production with confidence.

Implementation Basics

FastAPI applications are built around a few core patterns:

Path operations define your endpoints. Decorate async functions with @app.get(), @app.post(), etc. FastAPI routes requests to the appropriate handler and injects dependencies.

Pydantic models validate request and response data. Define a class inheriting from BaseModel with typed attributes. FastAPI validates incoming JSON against the model and serializes responses automatically.

Dependency injection manages shared resources. Database connections, API clients, and authentication can be injected into endpoints. This keeps your handlers focused on business logic.

StreamingResponse enables real-time LLM output. Instead of waiting for the complete response, stream tokens as they arrive. Users see text appear progressively, improving perceived latency.

Start with a simple endpoint returning LLM responses. Add Pydantic models for structured output. Implement streaming for chat interfaces. Use dependencies for API key validation and rate limiting. The built-in /docs endpoint provides interactive API documentation for testing during development.