FastAPI development and API engineering
We build fast, typed Python APIs with FastAPI and host them on AWS, over a PostgreSQL data layer, built by senior engineers.
Async by design, fast under concurrency
FastAPI is built on async Python, so it handles many concurrent requests without tying up workers. We use that where it matters most: APIs with heavy I/O, real-time endpoints, and services that fan out to other systems. The framework stays out of the way, which keeps services small, quick, and cheap to run.
- Async endpoints for high concurrency
- Great fit for I/O-heavy APIs
- Lean services, low overhead
Typed contracts with Pydantic
Every endpoint is described with Python type hints and Pydantic models, so requests are validated automatically and the OpenAPI docs are always accurate. That means fewer runtime surprises, a self-documenting API your clients can rely on, and contracts that are easy to evolve without breaking consumers.
- Automatic request validation
- Always-accurate OpenAPI docs
- Contracts that are safe to evolve
Data access that keeps up
We pair FastAPI with async SQLAlchemy and proper connection pooling over PostgreSQL, and cache with Redis where it helps. Heavy work runs in background tasks so the request path stays quick. The point is to make sure the database is not the thing that slows your fast API down.
- Async SQLAlchemy with pooling
- Redis caching where it helps
- Background tasks off the request path
Hosted on AWS
We run FastAPI on AWS ECS Fargate behind gunicorn with uvicorn workers, packaged in Docker and released through CI/CD pipelines. Everything is defined as infrastructure as code, so the service is repeatable, monitored, and scales without a manual deploy ritual.
- ECS Fargate with uvicorn workers
- Docker and CI/CD
- Infrastructure as code
Part of the wider stack
We run FastAPI alongside Django, a Next.js front end, and AI integration. You can also hire a senior Python developer directly.
FastAPI questions
For high-throughput async APIs, microservices, real-time endpoints, and backends for AI or data workloads. For content and admin-heavy products, Django is often the better fit, and we frequently run the two together.
Yes. With gunicorn and uvicorn workers, async database access, sensible timeouts, and monitoring, FastAPI runs reliably in production. We deploy it on AWS ECS Fargate as infrastructure as code.
It is an excellent fit. Async I/O suits calling LLMs and retrieval services, so we often build the API behind AI features in FastAPI as part of our AI integration work.
Let's build, scale, or automate it.
Hire a full senior team or a single specialist across frontend, backend, AWS, DevOps, and AI. You work directly with the engineers, not an account manager.