Find avoidable LLM spend and measure each change
How it works
Why it beats the alternative
How it actually works
Exactly what it does — and when.
A team sees provider usage rise on similar workload. They separate retry, duplication, cache, output, and routing observations, test one change against matched tasks, and retain it only when accepted-task quality holds. The playbook supplies the audit sequence, not a guaranteed percentage reduction.
Use it to…
Audit retry waste
Inspect empty responses and repeated attempts before changing model quality or capacity.
Right-size model routes
Move mechanical tasks to an appropriate configured model and compare accepted-task results.
Measure a change
Track observed waste and quality under the same task contract instead of assuming the playbook guarantees savings.
The shift it creates
- Silent failures you can't see
- Finding out after it breaks
- No repeatable QA pass
- Cut what COSTS, never what THINKS
- Prompt-cache alignment — the biggest free win
- Right-size the model per task
What's inside
Who it's for
Teams investigating provider usage
need a structured audit before changing quality or capacity
Agent-router maintainers
want to compare task-fit models on matched accepted work
Developers tracking retries
need avoidable attempts separated from useful tokens
Questions, answered
Ready to put The AI Cost-Cutting Playbook to work?
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