FastAPI.
FastAPI is a Python web framework built on type hints. Request and response models are Python types, validation and serialisation are derived from them, and OpenAPI documentation is generated automatically from the same source.
Where FastAPI fits on a long engagement.
Deriving validation and documentation from type hints removes a class of drift that plagues API work. The schema cannot disagree with the implementation because it is generated from it, so documentation that is wrong is no longer the normal state of an API six months after launch.
It is the standard choice for serving machine learning models, because the surrounding ecosystem is already Python. That avoids the rewrite step where a model is reimplemented in another language for serving, which is where a great deal of machine learning work has historically stalled.
What an assigned team does with FastAPI.
The async model is where teams get into trouble. A blocking call inside an async endpoint stalls the event loop and degrades the whole service, not just that request, and the symptom is general slowness rather than an obvious error.
Knowing which libraries are safe in that context is practical experience rather than documentation, and it is the kind of depth built through the Talent Success and Academy programme.
What we use FastAPI for.
- Documentation that cannot drift OpenAPI generated from the same types that validate requests.
- Model serving without a rewrite Python end to end, so a trained model reaches production in the language it was built in.
- Async used correctly Blocking work moved off the event loop, so one slow call does not degrade the service.
How FastAPI capacity is assigned.
FastAPI capacity is assigned under software development outsourcing, and alongside AI capacity where the service exists to serve a model.
Tell us what your roadmap needs FastAPI for.
A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.
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