FastAPI is a Python framework for building APIs. It validates every request against a typed schema and generates interactive documentation from that schema automatically. It is what we put in front of a machine-learning model when the rest of the application lives elsewhere.
What is FastAPI?
FastAPI does one job: turn Python functions into HTTP endpoints, with the request and response shapes declared as types. Because those types are declared, three things happen without extra work — requests are validated before your code runs, malformed input gets a clear error rather than an exception, and interactive API documentation is generated and stays accurate.
That last point matters more than it sounds. API documentation written by hand is out of date within a month; documentation derived from the code cannot be. When a mobile team and a web team are both consuming the same API, that is the difference between integration going smoothly and a week of misunderstandings.
It is also genuinely fast for Python — asynchronous by default, so it handles waiting on a model or a database without tying up a worker.
When we choose FastAPI
Whenever the AI work needs to be reachable over HTTP. The model is Python; wrapping it in a Python service avoids a language boundary, and FastAPI is the lightest correct way to do that.
When two or more clients consume the same API — a web app and a mobile app, or a partner integration. The generated documentation and strict validation are worth most exactly there.
And when the service is genuinely just an API with no interface of its own. Django would bring a great deal that goes unused.
It is also our default wrapper around anything Python does for a project that is otherwise PHP or Node. The main application calls the service over HTTP with a documented, typed contract, and neither side has to know how the other works internally.
When we do not use FastAPI
When the application needs an admin interface, accounts and content management. FastAPI gives you none of that, and building it by hand costs far more than Django's comes for free.
And when there is no Python in the project at all. Adding a Python service to a PHP or Node system means a second runtime to deploy, monitor and patch, which is only worth it when Python is doing something the other language genuinely cannot.
What we build with FastAPI
The API in front of a retrieval assistant
Question in, retrieved passages and a grounded answer out, with the evaluation harness running against the same endpoints. Typed request and response shapes mean the web team can integrate against the documentation before the model is finished.
Document and media processing endpoints
Upload a PDF or an audio file, get structured data back. The long work goes to a queue and the endpoint returns a job reference immediately, so a slow document never holds a browser connection open.
Model-serving endpoints
A typed API in front of a retrieval assistant, a classifier or a transcription pipeline, with long-running work handed to a queue so a request never sits open for forty seconds.
Integration services
A small service that sits between two systems that were never designed to talk — translating formats, reconciling identifiers and logging every exchange so a disagreement about what was sent has an answer.
How we ship FastAPI projects
Behind an API key or a signed token, always — an AI endpoint with no authentication is a bill someone else can run up on your account.
Rate limited per client, because the failure mode of an expensive model endpoint is not a crash, it is an invoice.
And with the generated documentation published somewhere the other teams can reach, since that is most of the reason for choosing this framework.
What FastAPI costs you
It is a framework for APIs and nothing else. Every non-API concern — admin, auth, background jobs, templates — is something you assemble yourself, and on a project that grows into needing all of them you will wish you had started with Django.
Asynchronous Python is also easier to get wrong than synchronous Python: one blocking call in an async handler stalls the whole event loop, and the symptom is a service that is mysteriously slow under load rather than one that errors.
It is a framework for APIs, not for websites — there is no admin, no templating story worth using and no batteries included. That is deliberate and it means anything beyond the API is your problem to solve elsewhere.
It is also younger than Django or Flask, so there are fewer worked examples for unusual problems. In practice that has not slowed us down, and it is worth knowing rather than discovering.
FastAPI questions we get asked
You can, if you are only calling a hosted model API — that is an HTTP request and PHP makes those fine. A separate Python service is worth it when you are running models locally, using Python-only libraries, or building an evaluation harness. We will tell you which situation you are in.
FastAPI validates request and response shapes from the type hints and generates the API documentation from the same source, so the docs cannot drift from the code. On a service another team consumes, that alone is worth the choice.
Only if there is real Python work — a model, a document pipeline, a library that only exists there. If the requirement is calling a hosted model API, your existing Laravel or Node application can do that with an HTTP request and no second service.
FastAPI generates interactive documentation from the code itself, so it cannot go out of date the way a written document does. Your developers get a browsable page where they can try each endpoint with real requests before writing a line against it.