Why inference becomes expensive
Inference happens every time a user, workflow or agent asks for an answer. A small inefficiency multiplied by thousands or millions of requests becomes a major operating cost.
The most common issues are long prompts, excessive RAG chunks, using the strongest model by default, repeated failed calls and serving duplicated questions without cache.
The ML Mind approach
ML Mind controls inference through context optimization, model routing, verified cache, retry prevention and fallback strategies based on the actual failure category.
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.