What metrics do you autoscale inference pods on, and how do you handle cold starts?
CPU-based HPA on GPU inference never fires, the trap half of all candidates fall into within a minute. The signals that actually track load, the anatomy of a five-minute cold start, and which mitigations are worth their cost at each layer.
Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.
CPU-based HPA on GPU inference never fires, the trap half of all candidates fall into within a minute. The signals that actually track load, the anatomy of a five-minute cold start, and which mitigations are worth their cost at each layer.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
