High-Performance Python APIs with FastAPI

A focused FastAPI backend — async endpoints, Pydantic schemas, PostgreSQL with SQLAlchemy, JWT auth, and Docker deployment — typically runs $30,000–$55,000 for a 10–14 week build with a two-engineer team. AI inference endpoints (wrapping an LLM or custom ML model) add $15,000–$25,000 depending on model serving complexity. Hourly rates for dedicated FastAPI engineers range from $65–$95/hour for senior-level work. We price after a discovery sprint so the estimate reflects your actual requirements.

FastAPI is production-grade and used in high-traffic systems at companies like Microsoft, Uber, and Netflix. Performance depends more on your architecture than the framework: proper async database drivers (asyncpg), connection pooling, Redis caching for hot paths, and horizontal pod scaling are what determine throughput. We size and load-test every FastAPI deployment against your expected traffic profile before handoff — throughput targets are defined and validated during the engagement, not discovered in production.

We default to SQLAlchemy 2.0 with its async session support and asyncpg as the PostgreSQL driver for async endpoints. For read-heavy reporting queries we sometimes use raw SQL via asyncpg directly for performance. We write explicit migrations with Alembic and version them in source control. For document-centric workloads (MongoDB, DynamoDB), we use Motor (async MongoDB driver) or the appropriate async client. The choice is dictated by your data model and consistency requirements, not habit.

Yes — it's one of the strongest use cases. We deploy ML models via FastAPI using background lifespan events for model loading (so the model is in memory before the first request), Pydantic schemas to validate input features, and response caching (Redis) for expensive predictions on repeated inputs. For high-volume inference we evaluate whether a dedicated serving layer (Triton Inference Server, Ray Serve) is more appropriate than FastAPI directly, and we'll recommend that honestly if your traffic profile warrants it.

Adding FastAPI to an existing Python codebase (e.g., extracting logic from a Django monolith or Flask app into a dedicated service) typically takes 4–6 weeks for a well-scoped extraction covering 10–20 endpoints. This includes refactoring business logic into testable service functions, writing Pydantic schemas for all request/response types, configuring async database access, and setting up the CI pipeline. We assess the existing codebase first — coupling and test coverage heavily affect the estimate.