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Take your FastAPI AI app from localhost to production

Streaming that survives proxies, background jobs that survive redeploys, health checks that mean something, JSON logs with a trace id, a slim Docker image, and a shutdown sequence that does not drop in-flight work.

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Taught by an engineer who has shipped this

  • ISO 27001

    Led the engineering work behind the certification of a regulated EU platform.

  • Series A platform

    Architected the case-management product that became the business a €11.6M round was raised on.

  • 3x faster deploys

    Cut time-to-deploy by migrating to Kubernetes on GCP with deploy-on-merge.

Take a FastAPI AI service from localhost to production. Stream tokens with SSE, run background jobs, lock down CORS, wire liveness and readiness probes, trace requests with JSON logs, ship a slim multi-stage Docker image, and drain work on SIGTERM.

Production FastAPI patterns for AI apps: SSE, jobs, CORS, probes, logs, Docker, graceful shutdown.

What you'll ship

Real projects, not toy demos.

  • An SSE streaming endpoint with heartbeats that survives proxy timeouts
  • A background job queue with idempotency keys and status polling
  • CORS and security header middleware with an explicit allow-list
  • Liveness and readiness probes wired to real dependency checks
  • Request-ID middleware that threads one trace id through every JSON log line
  • A multi-stage Dockerfile on a slim base with a non-root user
  • A lifespan handler that drains in-flight background tasks on SIGTERM

What you'll learn

You finish able to:

  • Stream LLM tokens with SSE and keep connections alive through proxies using heartbeats
  • Run async background jobs with idempotency keys, status polling, and failure recovery
  • Configure CORS with an explicit allow-list and add baseline security headers
  • Wire liveness and readiness probes that mean different things to the orchestrator
  • Emit JSON logs with a request id threaded through every record and across services
  • Build a multi-stage Dockerfile that ships a slim, non-root runtime image
  • Drain in-flight background work inside a FastAPI lifespan when SIGTERM arrives

Curriculum

From a naked FastAPI endpoint to a production-grade deployable AI service.

  1. 01

    SSE streaming

    Stream LLM tokens with Server-Sent Events, keep connections alive with heartbeats, and consume the stream from a client

  2. 02

    Background job queue

    Run async background work with idempotency keys, status polling, and failure recovery

  3. 03

    CORS and security headers

    Configure middleware for cross-origin requests and baseline security response headers

  4. 04

    Liveness vs readiness

    Expose two health endpoints that tell the orchestrator two very different things

  5. 05

    Request-ID and JSON logs

    Thread a trace id through every log record and emit structured JSON lines you can actually query

  6. 06

    Multi-stage Dockerfile

    Build on a slim base with uv, drop privileges, and ship a small runtime image

  7. 07

    Graceful shutdown

    Drain in-flight background jobs and close SSE streams cleanly when SIGTERM arrives

Who it's for

Is this for you?

Python backend engineers

who have shipped FastAPI apps but have never owned one in production with real traffic

AI engineers

whose streaming endpoint works on a MacBook and breaks behind nginx

Platform engineers

who need a reference implementation of the deployment concerns they audit on every AI service

FAQ

Common questions.

  • Do I need Kubernetes to follow along?

    No. The patterns are covered with FastAPI and Docker Compose. Liveness and readiness probes map directly to Kubernetes, ECS, Nomad, or any orchestrator that supports HTTP health checks.

  • Does this depend on a specific LLM provider?

    No. The workshop repo uses a small provider abstraction so you can run with OpenRouter, Fireworks, Gemini, or OpenAI. The deployment patterns are provider agnostic.

  • Do I need a GPU or paid cloud account?

    No. Everything runs on your laptop with Docker Desktop or Colima. The only paid piece is whichever LLM API key you already have.

  • Will these patterns work for a non-AI FastAPI service?

    Yes. SSE, background jobs, CORS, health probes, JSON logs, multi-stage Docker, and graceful shutdown apply to any async Python service. AI just makes the pain more obvious because tokens are slow and streams are long.