Stop Overspending: A Practical Guide to Kubernetes Autoscaling

Kubernetes autoscaling (primarily via the HPA) is powerful — but poorly tuned defaults can create instability, performance issues, and unnecessary cloud costs.

A common failure mode is flapping:
rapid scaling up and down caused by tightly configured thresholds.

🔹 The real challenge

Spiky but predictable traffic patterns cause HPA to over-react.
When the spike ends, HPA scales pods down too quickly…
only to scale back up on the next spike.

This behaviour wastes compute and degrades user experience.

🔹 A practical improvement

Increase the scale-down stabilization window.
This delay prevents premature downscaling and keeps capacity ready for the next expected peak.

Combined with sensible CPU/memory thresholds and metrics smoothing, this creates a far more stable workload behaviour.

🔹 The outcome

• Lower compute waste
• Smoother traffic handling
• Fewer cold-starts
• Improved reliability

Autoscaling is not “set and forget.”
It’s a discipline — one that pays off with both cost efficiency and performance stability.