Cost Optimization

Most teams overspend on GPU compute by 40 to 60 percent, and it's rarely because the hourly rate is wrong. It's idle capacity, the wrong billing model, or a workload that could run on a smaller GPU than the one it's on. This cluster is the fix for that: practical cost engineering, not pricing comparisons, which live in the GPU Pricing cluster instead.

Topics range from billing-model selection (serverless versus on-demand versus reserved, when spot's 40-80% discount is worth the interruption risk) to hardware right-sizing (fractional GPUs with MIG, MPS, and vGPU, heterogeneous inference that splits prefill and decode across different GPU types) to the costs teams miss entirely: egress fees, electricity draw, and the multiplied token cost of agentic workloads that burn 5 to 30 times more tokens than a single chat completion.

The pillar post, The GPU Cloud Cost Optimization Playbook, ties these together into one framework covering instance selection, spot strategy, and idle-GPU elimination. Read it first for the overall approach, then use the specific posts here, spot arbitrage, FinOps chargeback, rent-vs-buy TCO, for the piece of your bill that's actually the problem.