Generic Kubernetes cost tooling optimizes toward observed steady state, which is correct for most fleets and wrong for a seasonal business. When your year has a hard peak, the run-up to that peak is precisely when you want headroom that an optimizer reads as waste. Trusting a generic autoscaler through that window means either overriding it constantly or discovering its opinion during your highest-traffic hour.
Kubernetes-native intelligent autoscaling on CRD-based controllers, with Karpenter handling provisioning and consolidation — tuned for a demand curve generic tooling can't model. Right-sizing where it's safe, deliberate headroom where it isn't, and the policy expressed as a custom resource so teams could reason about it in the same place they reason about everything else.
Trust. Autoscaling changes are invisible when they work and career-defining when they don't, so adoption was a change-management problem more than a technical one. Getting 1,500+ services onto it meant making the default conservative and the reasoning legible, not making the algorithm clever.
Every vendor in this space demos the steady-state case, and the enterprises with the largest bills are frequently the ones with the least steady-state traffic — retail, tax, ticketing, anything with a season. That gap gets treated as an edge case. It's the opposite: it's where the money is and where generic products are weakest.