AI waste is rarely one thing
Most teams first notice the bill, but the real causes are distributed across product, engineering and infrastructure decisions. The diagnostic makes those causes visible.
- Tokens and prompt/context size.
- RAG chunk quality and freshness.
- Retry loops and failed tool calls.
- Model routing and provider choices.
- Cache opportunities and repeated answers.
- GPU utilization and serving design.
Likely savings map
Select your workload signals to generate a directional waste map.
Want ML Mind to validate this against real telemetry?
The free audit turns this directional diagnostic into a real savings map.