FastAPI is the framework we reach for when a Python API needs to be fast, well-typed, and ready to sit in front of an LLM pipeline. We build the API layer, wire it to your model or automation logic, and hand over something your team can extend without guessing what a route actually returns.
FastAPI trades Django's batteries-included scope for speed and type safety — the right trade when the API is a thin, fast layer in front of a model or pipeline.
Built on Starlette and async/await, FastAPI handles concurrent LLM calls and I/O-bound automation without blocking — no bolted-on async support.
Pydantic schemas validate input and output at the boundary, catching malformed payloads before they reach your model or business logic.
Every endpoint ships with interactive Swagger and ReDoc docs generated from your code — no separate spec to maintain or fall out of sync.
No ORM, admin panel, or templating engine you don't need — just routing, validation, and dependency injection for a focused API service.
The people scoping your endpoints are the people writing them — no handoff gap between architecture and delivery.
Scoped to the API you actually need — not a generic backend package.
A FastAPI service in front of your OpenAI, open-source, or custom model pipeline — request validation, streaming responses, rate limiting.
Turning internal scripts and automation logic into callable, documented endpoints your other systems can integrate with.
Extracting a high-traffic or latency-sensitive service from a Django monolith into its own FastAPI microservice.
OAuth2/JWT auth, request throttling, structured logging, and error tracking wired in from day one.
Containerized deployment behind Uvicorn/Gunicorn, sized and load-tested for your expected traffic.
Django is built for content-heavy apps with an ORM, admin panel, and templating you likely don't need for an API. FastAPI is async-first and lighter, which matters when you're proxying calls to an LLM and waiting on network I/O rather than serving pages.
Yes. We commonly build the FastAPI layer that handles request validation, auth, streaming responses, and rate limiting in front of an existing model or LLM provider — so your pipeline isn't exposed directly to clients.
Yes, it comes with the framework. Every endpoint gets typed request/response schemas via Pydantic, and interactive Swagger/ReDoc docs are generated automatically from your code rather than maintained separately.
A focused API — a handful of endpoints in front of a defined pipeline or data source — can typically go from scoping call to a working MVP in a few weeks. We give a fixed estimate after understanding what the API needs to do.
Tell us what your API needs to do — front an LLM pipeline, expose automation logic, or replace a slower framework — and we'll scope an approach and a fixed estimate.