Start with a practical estimate
Before a full audit, teams can estimate savings by entering request volume, token size, retry rate, RAG share, cache opportunity, model mix and GPU utilization.
Use the result as a discovery map
A calculator does not replace a live audit, but it shows which levers are likely to matter most: tokens, RAG, routing, retries, cache, GPU serving or training lifecycle.
How to apply this with ML Mind
Use this topic as a discovery lens. Start by identifying the workflow, measuring the current waste pattern, then deciding whether the right control is visibility, pre-model optimization, full gateway control, ModelOps serving control or lifecycle governance.
Recommended next step: open the related simulator or calculator, test the pattern with your approximate numbers, then request a deployment review if the savings lever appears material.