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FastAPI

Modern, fast Python APIs

FastAPI is my preferred Python web framework. Pydantic-based validation, async-first design, and automatic OpenAPI generation make it the cleanest way to ship a Python service in 2026.

4+years in production
30+projects shipped
expertproficiency

My take

Why I use FastAPI

FastAPI gets out of my way. Type hints become validation, docs, and serialization simultaneously. For AI inference services or any Python-native API, it's the most productive option I've used.

Want the broader stack philosophy? Read about how Sri picks tools or browse engineering insights.

Honest assessment

Strengths & tradeoffs

No tool is perfect. Here's what shines and what to watch for.

Strengths

  • Pydantic validation tied to type hints
  • Auto-generated OpenAPI/Swagger docs
  • Async-first for I/O-bound workloads
  • Dependency injection system is genuinely useful
  • Excellent performance via Starlette and Uvicorn

Tradeoffs (honestly)

  • Async Python has sharp edges (sync code blocks the loop)
  • Less batteries-included than Django
  • Smaller ecosystem of plugins vs. Flask

Fit assessment

When to reach for FastAPI

Pick the right tool for the job.

Best fits

Serving ML/AI models via HTTP

Internal microservices in a Python stack

Webhook receivers and integration layers

Data-heavy APIs leveraging Pydantic

RAG and agent backends

Not ideal for

Teams already deeply invested in Django

Sync-heavy workloads with no I/O concurrency benefit

Common use cases

AI inference APIsInternal microservicesML model servingWebhook handlers

Resources

Learn more

Curated official docs, tutorials, and writing on FastAPI.

Stack

Pairs well with FastAPI

Tools and platforms I commonly combine with this one.

Need help with FastAPI?

Whether you're starting fresh or optimizing an existing implementation, I can help you get the most out of this technology. Read more in insights or get in touch.

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