Hire FastAPI Engineering
for high-performance APIs

From async REST microservices to AI model serving endpoints, our FastAPI engineers build type-safe, self-documenting APIs that scale under production load.

Discuss your FastAPI project

30+

FastAPI projects delivered

years of Python API expertise

50+

Python & API engineers

Core Capabilities

What we build
with FastAPI

Async REST & GraphQL APIs

Type-safe, auto-documented endpoints

High-performance async APIs with Pydantic v2 validation, automatic OpenAPI/Swagger documentation, and ASGI deployment on Uvicorn — handling thousands of concurrent requests without blocking.

Microservices Architecture

Scalable, containerized services

FastAPI microservices with Docker and Kubernetes — event-driven communication via Kafka or RabbitMQ, service mesh integration, distributed tracing, and centralized observability across your stack.

AI Model Serving APIs

LLM and ML inference endpoints

Production AI backends that serve PyTorch, TensorFlow, and Hugging Face models via FastAPI — with streaming SSE responses for LLMs, background inference queues, and model versioning support.

How It Works

From spec to
production API

API Design &
Schema Planning

We define your API contract first — endpoints, request/response schemas, authentication flow, and error handling — using Pydantic models and OpenAPI spec before writing a single line of logic.

Agile
Development

Our Python engineers work in 2-week sprints with continuous integration and demo cycles. You see working endpoints every step of the way.

Testing &
CI/CD

Comprehensive test suites with pytest and HTTPX async test clients. Our QA specialists and DevOps engineers automate load testing with Locust and gate every build.

Deployment &
Monitoring

We deploy FastAPI on Kubernetes with Uvicorn workers, configure health checks, set up rate limiting, and monitor performance with Prometheus, Grafana, and distributed tracing via OpenTelemetry.

Hire FastAPI Developers

FastAPI engineers ready
to join your team

Grow your backend team with dedicated FastAPI developers who build high-throughput, production-ready APIs from day one.

Hire FastAPI Developers

Async REST & GraphQL API design with Pydantic v2

AI & ML model serving endpoints with streaming responses

Microservices with Docker, Kubernetes & Kafka

SQLAlchemy & Alembic database integration and migrations

OAuth2, JWT authentication & API security best practices

AI + FastAPI

APIs that don't just
serve — they think

LLM streaming
endpoints

FastAPI's server-sent events and streaming responses are ideal for LLM applications — enabling real-time token streaming from GPT, Claude, or open-source models without client timeouts.

AI-generated
API tests

Automated test generation for FastAPI endpoints — schema-aware test cases, edge case discovery, and contract testing that catches regressions before they reach production.

Intelligent
observability

OpenTelemetry tracing, Prometheus metrics, and AI-powered anomaly detection on your FastAPI services — automatically surfacing latency spikes and error patterns before users notice.

AI-assisted
optimization

AI-driven profiling to identify slow database queries, N+1 problems, and async bottlenecks in your FastAPI application — with automated recommendations for connection pool tuning and cache strategy.

FAQ

Frequently Asked
Questions

FastAPI is purpose-built for APIs — it is async-first with native Python type hints, generates automatic OpenAPI documentation, and achieves performance comparable to Node.js and Go. For teams building high-throughput APIs or AI backends where latency matters, FastAPI outperforms Django REST Framework and Flask significantly.

Yes. FastAPI is built on ASGI (Uvicorn/Gunicorn) and handles thousands of concurrent requests with async I/O. We deploy FastAPI applications on Kubernetes with horizontal autoscaling, Redis caching, and connection pooling to handle enterprise-scale traffic reliably.

FastAPI is the leading framework for serving AI/ML models in production. We use it to wrap TensorFlow, PyTorch, and Hugging Face models as REST APIs — with async inference, background task queues via Celery, streaming responses for LLMs, and OpenAPI documentation for easy integration.

Our FastAPI stack includes Pydantic v2 for data validation, SQLAlchemy or Tortoise ORM for databases, Alembic for migrations, Celery for background tasks, Redis for caching, pytest for testing, and Docker/Kubernetes for deployment. We also integrate with LangChain and LlamaIndex for AI-powered endpoints.

Absolutely. We have migrated Flask and Django REST Framework APIs to FastAPI — retaining business logic while gaining async performance, automatic type validation, and OpenAPI documentation. Migrations are done incrementally to avoid downtime.