How should I tune Kubernetes autoscaling metrics to avoid overprovisioning?

[Brian_W]

We're finally moving a few of our core services into containers, and I'm trying to wrap my head around the scaling setup. I've got the basics of kubernetes cluster autoscaling configured, but I'm nervous about letting it manage itself completely. How do you decide on the right metrics and thresholds so it doesn't either over-provision wildly or fail to react to a real traffic spike?

[Aubrey80]

Totally get the nerves about scaling up automatically and not chasing ghosts I would not rely on a single metric The key is to pick a baseline like CPU usage and then add a couple of signals that fit your service Also remember to look at response times and error rates

[OliverG]

In practice you want a mix of pod level metrics and cluster signals and you should set clear min and max for both nodes and pods Use the HPA for pods and the cluster autoscaler for nodes and give each some breathing room

[Sophia55]

Watch for thrashing if thresholds are too tight If the autoscaler keeps spinning up and down you pay more and your caches suffer A stabilization window and a deliberate scale up delay help keep things sane

[James_M]

CPU is a good start but add memory and a few workload specific metrics like queue length or request latency If you have async tasks or a background job you can scale on those signals too

[AbigailYJ]

Set up metrics collection with a tool you trust and keep it simple Prometheus or the cloud stack can work The goal is to see where you are over or under provisioned before it bites

[MilaR]

Put a dry run plan and test in staging before you go live and simulate spikes You want a runbook that says what to adjust and when without panicking