The five major cost drivers
Modern ML cost comes from compute, inference, storage, networking and governance overhead. GenAI adds additional drivers: prompt length, retrieved context, provider pricing, tool calls, retry loops, cache misses and model escalation.
Why governance matters
A cost driver becomes controllable only when it can be attributed to a workflow and governed by policy. ML Mind connects usage telemetry with request-path decisions so cost can be reduced without breaking quality.
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.