Autoscaling
Scaling on the signal that reflects the actual constraint, and the lag, cold starts, oscillation and downstream bottlenecks that make autoscaling a capacity tool rather than a capacity answer.
A control loop from a metric to a policy to more or fewer instances — with lag and warm-up as first-class properties rather than details.
Scaling too late, on the wrong signal, into a downstream bottleneck, or against itself — the six failures that produce most autoscaling incidents.
One concrete implementation of the control loop — how Kubernetes does it, and what a VM autoscaling group or a serverless platform does instead.
For workers, queue depth and message age describe the constraint far better than CPU ever will.
Running nothing when there is nothing to do — and paying for it with a cold start on the next request.
The metric you scale on decides whether autoscaling works at all — and CPU is the wrong one for most APIs.