Many are not. Default configurations dominate because they are safe defaults. Engineering leaders hesitate to change what works, even if it works poorly. The fear of breaking production outweighs the benefit of optimization. However, with better observability tools available today, the risk is manageable. You can test custom metric scaling in staging environments long before pushing to production.

Testing in staging is key. What about the reverse problem? Overscaling. When traffic drops, do pods terminate quickly enough?

That is the other half of the equation. Downscaling is often slower than upscaling due to safety buffers.

It is a behavioral economics problem disguised as a technology stack decision.

How do you fix that without breaking the site during a real crisis? You can't just turn off the lights and hope no one notices.

The solution requires moving from static reservations to dynamic, policy-driven shutdowns. We saw a company recently implement what they call 'granular pod lifecycle management.' Instead of letting Kubernetes decide when to kill a pod based solely on memory pressure, they built a custom controller that monitors request volume per endpoint. If an endpoint has zero requests for fifteen minutes, the controller scales that specific deployment to zero replicas.