Hey everyone,
My team and I are currently deploying n8n in Queue Mode (Main + Workers via Redis) on Google Cloud Run. We aim for a serverless, scale-to-zero (ornear-zero) architecture to keep idle costs low.
However, we hit a fundamental architectural wall:
Google Cloud Run’s native autoscaler works by monitoring incoming HTTP requests. But the n8n-worker instances don’t receive HTTP traffic (except maybe a health check ping); they actively pull jobs from the Redis queue.
Because Cloud Run is completely blind to the Redis queue depth, it never automatically scales out the worker instances, even if 100 heavy jobs are piling up in Redis. Cloud Run just sees “0 HTTP requests” and keeps the worker at 1 instance (or scales it to 0).
My Questions to the community:
- How are you horizontally scaling your n8n workers on Cloud Run?
2. Are you using external metric autoscalers (like CREMA / KEDA) to ping the Google Cloud API based on Redis queue depth? (Seems very complex for a serverless setup).
3. Or did you give up on Queue Mode for Cloud Run and went back to standard mode?
Note: We currently found a workaround by strictly limiting the worker to 1 vCPU and using a continuous health-ping to keep the container awake. By doing this, heavy jobs force the single CPU to 100%, which successfully tricks the Cloud Run CPU-autoscaler into spinning up more instances. It works well, but I’m curious if there’s a more “official” or elegant solution out there!
Would love to hear your deployment strategies!