FinOps for GPU workloads: where the money actually goes

Idle GPUs are the new idle VMs, and they cost roughly ten times as much.

Find the waste first Instrument per-request token counts, per-feature spend and cache hit rate before optimizing anything. Most teams discover one or two features consuming the majority of the bill.

The controls that pay - Prompt caching for stable system prompts and shared context - Semantic caching for repeated user questions - Model routing: cheap model first, escalate on low confidence - Batching for anything asynchronous - Aggressive output token limits with structured outputs - Committed capacity only after usage flattens out

Trim the context window Context length drives cost quadratically in attention and linearly in price. Better retrieval usually cuts more spend than a cheaper model.

Budget per feature Give every AI feature a cost-per-request target and alert on it like latency. Cost is a product constraint, not a finance report.