Development

FastAPI Development Services

FastAPI is our go-to framework for async, high-performance Python APIs — especially backends that stream LLM responses, call multiple AI services concurrently, or need to handle real-time traffic at low latency. We build production FastAPI services with proper validation, auth, and background task handling, not just a quick prototype.

Built for async, AI-heavy workloads

FastAPI's async-first design and automatic request validation (via Pydantic) make it a strong fit for backends that spend a lot of time waiting on external calls — LLM APIs, vector databases, third-party services — since it can handle many concurrent requests without blocking. It also generates interactive API docs automatically, which speeds up integration for whoever's building against it.

Async by default

Handles high-concurrency workloads efficiently, especially I/O-bound calls to LLMs or external APIs.

Automatic validation

Pydantic-based request/response validation catches bad data before it reaches your business logic.

Auto-generated docs

Interactive OpenAPI documentation generated directly from the code, always in sync.

How we build a FastAPI service

1

API design

Define endpoints, request/response schemas, and auth requirements before implementation.

2

Build with validation

Implement with Pydantic schemas and proper error handling at every endpoint.

3

Async & background tasks

Wire up streaming responses, concurrent calls, and background job processing where needed.

4

Deploy & load test

Containerized deployment with load testing before go-live for anything traffic-sensitive.

Where FastAPI fits

LLM-backed APIs

Streaming chat completions and RAG endpoints that need to stay responsive under concurrent load.

AI Products

Microservices

Lightweight, independently deployable services within a larger backend architecture.

Enterprise

Real-time & webhook endpoints

High-throughput endpoints for webhooks, IoT data, or real-time event processing.

SaaS

Tools we build with

FastAPI Pydantic Uvicorn / Gunicorn PostgreSQL Redis Celery Docker

Why FastAPI for AI-integrated backends

Async handling means one slow LLM call doesn't block other requests from being served.

Built-in validation catches malformed requests before they hit your business logic or a costly AI API call.

Auto-generated docs speed up integration for frontend teams or external partners.

Lightweight enough for microservices, without sacrificing structure or type safety.

See what we've built

Recent projects

Browse our portfolio →

Frequently asked questions

When should I use FastAPI instead of Django?
FastAPI suits high-performance async APIs, especially AI-integrated backends where latency and concurrency matter. Django suits admin-heavy or content-heavy applications where you want a built-in ORM and admin panel.
Is FastAPI good for streaming LLM responses?
Yes — FastAPI's async support handles streaming responses well, which is important for chat-style AI interfaces where users expect a token-by-token reply instead of waiting for the full response.
Can FastAPI handle high traffic in production?
Yes, when deployed correctly with an ASGI server like Uvicorn behind Gunicorn workers, and load tested for your expected traffic before launch.
Do you provide API documentation?
Yes — FastAPI auto-generates interactive OpenAPI documentation directly from the code, which we deliver as part of the project.
How much does a FastAPI project cost?
It depends on the number of endpoints and integration complexity — a scoping call gives an accurate estimate.
How long does it take to build a FastAPI service?
A focused API service typically takes a few weeks including validation, testing, and deployment setup.

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