Backend & APIs
FastAPI for typed, high-performance Python services
The fastest way to expose Python logic as a documented, validated, async API that front-end and integration teams can consume without guesswork.
Auto
OpenAPI docs generated from the code
3-5x
Throughput versus synchronous Python frameworks
0
Undocumented endpoints in delivered services
<100ms
Overhead budget outside model inference time
Definition
What FastAPI is
FastAPI is an async Python web framework built on Starlette and Pydantic. Type hints generate request validation and OpenAPI documentation automatically, so contracts stay accurate as the service evolves.
When to use it
Where FastAPI is the right call
Recognise your situation before committing engineering budget.
Capabilities
How we work with FastAPI
What is actually delivered, beyond the logo on a stack slide.
Schema-first contracts
Pydantic models validate every request and response and generate typed clients.
Async orchestration
Concurrent calls to models, vector stores and databases without blocking workers.
Streaming responses
Token streaming for chat and agent interfaces over SSE or websockets.
Production hardening
Rate limiting, auth, request tracing and graceful shutdown built into the template.
Strengths
- Documentation and validation come free with the type hints
- Excellent async performance for I/O-bound AI workloads
- Small, readable services that are easy to test
- Native fit with the Python AI ecosystem
Trade-offs to plan for
- Async code requires care — a blocking call stalls the event loop
- Not a batteries-included framework; auth and admin are your choices
- Long-running inference needs a queue, not a request thread
Use cases
FastAPI in production
Representative systems we build and support on this technology.
RAG query service
Retrieval, reranking and generation exposed as a single streaming endpoint.
Extraction API
Upload a document, receive validated structured JSON with confidence scores.
Scoring service
Credit, risk or demand scores served to ERP and CRM in real time.
Comparison
FastAPI vs Django vs Flask
| Dimension | FastAPI | Django | Flask |
|---|---|---|---|
| Async support | Native | Partial | Limited |
| Auto documentation | Built in | Add-on | Add-on |
| Admin and ORM included | No | Yes | No |
| Best fit | AI and data APIs | Content-heavy apps | Small scripts |
Engineering practices
Non-negotiables on every engagement
Decision guide
Which direction fits your situation?
Match your constraints to the recommended approach.
| If this sounds like you | We recommend |
|---|---|
| Exposing models or data logic to other systems | FastAPI service with generated clients |
| Need a full admin and CMS in Python | Django, with FastAPI for the AI endpoints |
| Inference takes longer than a few seconds | FastAPI plus a queue and worker pool |
Delivery
Services built on FastAPI
Pairs well with
Industries running this
FAQ
FastAPI questions, answered
Related technologies
What we usually pair with it
Python
The default language for AI, data and automation work — with a mature ecosystem for LLMs, document processing, forecasting and scientific computing.
Node.js
An event-driven runtime that handles thousands of concurrent I/O-bound requests efficiently — ideal for API gateways, integrations and real-time features.
Docker
Reproducible application images that remove "works on my machine" from the vocabulary and make deployments boring in the best possible way.
Planning a build or migration on FastAPI?
Share your requirements and current systems. We will return an architecture recommendation, trade-offs and a phased plan — under NDA.