AI FinOps
The practice of managing AI spend, unit economics, governance and accountability across AI workloads.
Integrity-adjusted savings
Cost reduction that only counts when answer reliability, facts, citations, policies and risk requirements remain protected.
RAG cost waste
Unnecessary cost caused by sending too many, stale, duplicate or irrelevant retrieved chunks to a model.
Semantic cache
A cache that recognizes similar user intent, not only identical prompts, while respecting freshness and source version.
Cheapest safe model
The lowest-cost model capable of answering a specific request under the required quality, risk and verification constraints.
Retry loop
Repeated attempts after a failure pattern, often multiplying tokens, latency and provider load without solving the root cause.
GPU serving waste
Idle replicas, low utilization, bad batching, OOM failures or overpowered models in self-hosted inference stacks.
Deployment level
The depth of ML Mind integration, from logs-only visibility to full gateway, ModelOps or training lifecycle control.