Why do Cloud Run containers stop processing concurrent FastAPI background tasks?

Below is an explanation of the behavior you’re seeing and some guidance on how to address it.

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What’s Likely Happening

Even though you’re using a “CPU always allocated” configuration, Cloud Run is built and optimized for request‐driven workloads. In this architecture, background work that continues after a response is sent isn’t managed by Cloud Run’s lifecycle. As a result, when you hand off work to a background task (via FastAPI’s background tasks) that isn’t awaited as part of the request, you may run into unexpected behavior:

• Container Reuse and Lifecycle: Once your HTTP request is complete, Cloud Run may eventually decide to recycle or shut down that container instance—even if a background task is still in progress. With a single background task this may sometimes “get away” with finishing its work, but when several are running concurrently, the likelihood increases that the container will shut down before all tasks complete.

• Background Work Outside the Request: Cloud Run expects that all significant processing happens during the lifecycle of an HTTP request. Offloading long-running work to a background thread can lead to situations where the work isn’t guaranteed CPU time (or even completed execution) if the container is recycled for a new request.

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Recommended Approaches

To achieve reliable background processing on Cloud Run, consider one of the following patterns:

1. Complete Work Within Request:
   Ensure that all necessary processing (including interactions with your database) is completed before the HTTP response is sent. In other words, rather than “fire and forget,” have your app await the asynchronous operations so that they are tied to the request lifecycle.

2. Use an External Task System:
   For long-running or asynchronous jobs, decouple the work from the HTTP request by using something like Cloud Tasks (or another queuing system). In this pattern, your FastAPI service would enqueue a job, and a separate service (or Cloud Run job) would process it. This ensures that work is not tied to the lifecycle of an HTTP request.

3. Cloud Run Jobs:
   If your background tasks are compute-heavy or need to run independent of the request, you might consider Cloud Run Jobs. This provides a more explicit model for background or batch operations and avoids issues related to container shutdown.

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Summary

Your local testing environment may not mirror the lifecycle constraints of Cloud Run, which is why background tasks succeed locally but can be prematurely terminated on Cloud Run—especially when multiple tasks run concurrently. Even with “cpu always allocated” settings and high resource limits, background threads that live outside the main request lifecycle aren’t a pattern that Cloud Run reliably supports. It’s best to tie the work to the request or offload it to an external system designed for asynchronous task processing.

If you need further clarification on best practices for background processing on Cloud Run, the documentation offers additional guidance for writing effective services and managing background activity in the context of Cloud Run’s execution model.