Start with visibility
Use telemetry and logs to discover where waste exists.
Pillar guide
LLM spend visibility that explains why cost happened.
A monthly bill tells you what was spent. AI spend visibility should explain which prompts, chunks, retries, models and workflows created the cost.
What to measure
- Requests, tokens, latency, provider and model choice.
- RAG chunk count, freshness, trust and citation value.
- Retry count, failure category and fallback path.
- Cacheable intent rate, cache hit rate and source version freshness.
- GPU serving utilization, OOM loops, batching and replica behavior.
ML Mind connects these signals to integrity-adjusted savings, so teams avoid cost cuts that break trust.
Move into control
Use pre-model or gateway integration to prevent waste in the request path.
Validate with audit
Turn savings opportunities into evidence, recommended controls and a pilot plan.
Turn this page into a validated savings map.
Use ML Mind to identify where AI spend is leaking, which controls are safe at your deployment level, and what evidence your team needs for an audit, pilot or executive review.