Why FastAPI over Django or Flask?

FastAPI offers automatic request validation with Pydantic, auto-generated OpenAPI docs, native async support, and performance comparable to Node.js and Go. Choose FastAPI for API-first backends; Django for full-stack applications with an ORM and admin panel.

Can FastAPI serve machine learning models?

Yes, FastAPI is the most popular framework for ML model serving. Its async support handles concurrent predictions efficiently, and Pydantic validates input/output schemas for your models.

Does FastAPI support WebSockets?

Yes. FastAPI has native WebSocket support for real-time communication, with the same dependency injection and middleware system used for HTTP endpoints.

FastAPI Development for High-Performance Python APIs

Our AI-managed teams build production-grade APIs with FastAPI, combining Python's ecosystem power with modern async performance and automatic documentation.

Technology: FastAPI (Backend Framework)

FastAPI is a modern, high-performance Python web framework for building APIs with automatic OpenAPI documentation, type validation via Pydantic, and async support. Our teams use FastAPI for building robust backend services, ML model serving endpoints, and data-intensive APIs that benefit from Python's rich ecosystem.

What We Build

REST and GraphQL APIs: Production-grade APIs with automatic request validation, OpenAPI documentation, and async request handling for high concurrency.
ML Model Serving Endpoints: API layers for serving machine learning models with batch prediction, model versioning, and A/B testing support.
Data Pipeline APIs: Backend services that orchestrate data ingestion, transformation, and delivery for analytics and reporting platforms.
Microservice Backends: Individual microservices in a larger architecture with gRPC or HTTP communication, health checks, and observability.

Expertise

FastAPI Projects Delivered: 50+
Average API Response Time: Under 50ms
OpenAPI Spec Coverage: 100% auto-generated
Test Coverage Standard: 90%+

Sample Projects

ML Prediction API: A model serving API for a fintech company that processes loan applications with real-time credit scoring and batch predictions. (200 hours)
- Real-time and batch prediction endpoints
- Model versioning and A/B testing
- Pydantic request/response validation
- Background task processing
Multi-Tenant SaaS API: A multi-tenant backend API with tenant isolation, rate limiting, API key management, and usage metering. (280 hours)
- Tenant isolation middleware
- API key authentication and rate limiting
- Usage metering and billing integration
- Auto-generated OpenAPI documentation
Data Ingestion Service: An async data ingestion service processing 10K+ events per second from IoT devices with validation and storage. (160 hours)
- Async event ingestion with validation
- Kafka producer integration
- Health check and metrics endpoints
- Structured logging with correlation IDs

Frequently Asked Questions

Why FastAPI over Django or Flask?: FastAPI offers automatic request validation with Pydantic, auto-generated OpenAPI docs, native async support, and performance comparable to Node.js and Go. Choose FastAPI for API-first backends; Django for full-stack applications with an ORM and admin panel.
Can FastAPI serve machine learning models?: Yes, FastAPI is the most popular framework for ML model serving. Its async support handles concurrent predictions efficiently, and Pydantic validates input/output schemas for your models.
Does FastAPI support WebSockets?: Yes. FastAPI has native WebSocket support for real-time communication, with the same dependency injection and middleware system used for HTTP endpoints.