Our stack has historically leaned heavily on JavaScript, but a growing share of internal tooling and data-adjacent work this year has been Python. With two solid framework options on the table, we needed a clearer rule than "whichever one someone likes," especially once three different teams had each picked a different default without ever comparing notes.
FastAPI's automatic OpenAPI docs and type-hint-driven validation have made it the obvious choice for anything exposing an API to other services or a frontend. Pydantic models double as both the validation layer and the documentation source, so the contract a consuming team reads is generated from the same code that enforces it at runtime, not a hand-maintained spec that drifts out of sync within a month. That alone has cut down on a category of bug reports where a field was silently renamed on one side and nobody noticed until production.
Flask still wins for small scripts wrapped in a web interface, where FastAPI's structure is more ceremony than the project needs. A one-off internal tool that scrapes a report, reformats it, and serves it on a single route doesn't benefit from response models and dependency injection; it benefits from being five files instead of fifteen. We've watched engineers reach for FastAPI out of habit on projects that would have shipped faster, and stayed simpler to hand off later, as a fifty-line Flask app.
Performance rarely factors into the decision the way outsiders might expect. Both frameworks are fast enough for internal tooling load, and the async support in modern Flask has narrowed whatever gap existed. The real differentiator is the shape of the problem: does this thing have an API contract that other services or a frontend depend on, or is it a script that happens to need a browser-accessible interface? The former goes to FastAPI. The latter goes to Flask, unless it later grows contracts of its own, at which point the migration path is straightforward enough that we don't worry about picking wrong on day one.
The type-hint-driven validation FastAPI builds on top of Pydantic has a second, less obvious benefit: it forces a project to actually define its data shapes up front rather than letting them emerge implicitly from whatever the first developer happened to write. On a Flask project, it's common for the "shape" of a request body to exist only as an assumption in someone's head, discoverable by reading the handler code. On a FastAPI project, that shape is a Pydantic model sitting in its own file, which means the second developer to touch the endpoint doesn't have to reverse-engineer the contract from request-handling logic.
We did run into friction points worth naming honestly. FastAPI's dependency injection system is powerful but has a real learning curve for engineers coming from Flask's simpler request-context model, and we spent more onboarding time than expected explaining `Depends()` to Python developers who'd never needed anything like it before. Async-by-default also means a developer who reaches for a blocking library inside an async route can silently stall the whole event loop, a mistake Flask's traditionally synchronous model makes structurally harder to make by accident.
Deployment considerations factored into a few decisions too. FastAPI's ASGI foundation pairs naturally with Uvicorn or Gunicorn-with-Uvicorn-workers, while a lot of our existing internal infrastructure was already tuned around Flask's WSGI deployment model. That wasn't enough to change the FastAPI-versus-Flask decision on its own, but it did mean a couple of early FastAPI projects took longer to get into production than they should have, simply because our deployment playbooks hadn't caught up to the new stack yet.
The short version: FastAPI for anything with a real API contract, Flask for quick internal utilities. Both beat reaching for a heavier framework, or a JavaScript backend, when the job doesn't call for one. Standardizing on this rule this year has mostly just saved us from re-litigating the same conversation on every new project kickoff.