Streaming LLM Applications with FastAPI
Stream LLM responses in real-time and master prompt engineering fundamentals.
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FastAPI is the default framework for Python services in 2026. It's fast, typed, and integrates cleanly with async code and Pydantic. The tricky parts aren't in Hello World tutorials: streaming large responses, handling file uploads without crashing, generating OpenAPI docs that stay in sync, and the deployment tricks that keep latency predictable.
Curated by Param Harrison
These courses focus on those production patterns. Start with FastAPI Fundamentals if you're new, or jump straight to specific recipes for what you're building.
Showing 23 of 23 courses
Common questions
FastAPI for API-first services, async workloads, LLM integrations, or strict typing. Django when the admin, ORM, and auth kit are what you came for. These courses assume FastAPI because that’s what most AI-adjacent backends pick today.
Domain-first folder layout, routers as thin wrappers, services for business logic, Pydantic schemas as typed boundaries. The Twelve-Factor FastAPI course covers an opinionated production layout.
Strictly v2 across every FastAPI course.
Depends on your use case. Session cookies for browser apps (Better Auth, Authlib), bearer tokens for machine-to-machine, OAuth for third-party integrations. Most courses assume a session provider is already configured and focus on the service code.
Use asyncpg for Postgres or aiosqlite for SQLite. The multi-llm-provider-pattern course uses Drizzle/Prisma only in the Next.js context; for Python services, SQLAlchemy async or asyncpg directly.