The default Kubernetes Cluster Autoscaler settings have probably cost you more than you think. I’ve seen them create more problems than they solve, oscillating between overprovisioning and performance throttling.
The tool isn’t the issue. The problem is treating autoscaling as a simple on/off switch. True cost efficiency comes from acknowledging the trade-offs between latency, availability, and spend.
My approach is to move away from a single, monolithic node pool. I prefer a tiered strategy:
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A baseline pool of reserved instances for predictable, steady-state services. This is our foundation.
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A separate spot instance pool for stateless, fault-tolerant workloads. High risk, high reward.
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A dedicated on-demand pool for critical, spiky services that need to scale fast without interruption.
This requires meticulous tuning of taints, tolerations, and pod anti-affinity. It also means your pod resource requests aren’t just suggestions—they are the contract that drives the entire system. Get them wrong, and the whole model collapses.
What’s the most subtle failure mode you’ve encountered when HPA and the Cluster Autoscaler interact under pressure?
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