How do you handle slow startup without blocking the FastAPI event loop?

WHAT THIS TESTS: This question tests whether you understand FastAPI lifespan events versus deprecated startup events, and whether you know how to keep the async event loop unblocked during heavy synchronous initialization. It also checks if you can design behavior for requests that arrive before the application is fully ready.

A GOOD ANSWER COVERS: A strong answer hits four things in order. First, use the modern async lifespan context manager rather than the deprecated startup event, which gives you structured setup and teardown around the application lifespan. Second, offload the blocking machine learning model loading to a thread pool using asyncio.to_thread or a loop run in executor so the event loop remains responsive while the heavy work runs. Third, introduce a global readiness flag or an app state variable that starts as false and flips to true only after the model is fully loaded and stored. Fourth, add middleware or a dependency that checks this flag on every incoming request and returns HTTP 503 Service Unavailable with a Retry-After header if the server is not yet ready, telling load balancers and clients to back off.

COMMON WRONG ANSWERS: The biggest red flag is loading the model directly inside the startup or lifespan handler without any threading, which blocks the event loop and prevents the server from accepting connections until loading finishes. Another red flag is suggesting background tasks for initialization, because background tasks run after the app has started and create a race condition where requests hit an uninitialized model. A third red flag is ignoring early requests entirely and assuming an external load balancer will solve the problem without explicit 503 handling.

LIKELY FOLLOW UPS: An interviewer might ask how you would handle initialization in a multi worker environment where each worker loads its own copy of the model. They might ask how to separate health checks from readiness checks, or how to gracefully degrade if the model fails to load. Another follow up is asking about memory implications of loading a large model in every worker and whether a separate inference service or shared memory would be better.

ONE CONCRETE EXAMPLE: Imagine a FastAPI app that loads a two gigabyte PyTorch model on startup. You define an async lifespan context manager that yields control only after the model is ready. Inside the lifespan block, you call await asyncio.to_thread passing the load_model function and its path argument, which runs the blocking torch.load call in a thread pool. You store the model in app.state.model and set app.state.ready to True. You then add an async dependency called verify_ready that raises an HTTPException with status 503 and a Retry-After header of ten seconds if app.state.ready is False. This lets Uvicorn bind the port immediately, keeps the event loop unblocked during the multi-second load, and gives early requests a clear signal to retry instead of timing out.