FastAPI Patterns That Keep ML Services Maintainable

FastAPI earns its keep when ML services stop looking like notebook exports glued to a route.

Use Pydantic models at the edge. Invalid payloads should die at validation, not inside a torch call.

Keep model load and warm-up out of request handlers. Cold start logic in a path handler is a future outage.

Separate inference from persistence and external APIs. Async I/O is great; blocking GPU work without a plan is not.

Return error shapes clients can parse. A 500 with a stack trace is for you, not for their product surface.