Reduce LLM Spend With Evidence, Not Hunches
ProofMap turns cost optimization into a measurable workflow: compare models, inspect failures, and promote the cheapest runtime that still passes.
Get StartedWhy Choose ProofMap
Find cheaper qualified models
Run challengers against production-like tests and compare pass rates with cost estimates.
Catch hidden quality costs
See the failures that would create support tickets, retries, or manual review before they become operational cost.
Make savings repeatable
Keep approved prompt packages and runtime mappings so cost wins survive future model changes.
Comparison
| Decision area | Ad hoc workflow | ProofMap |
|---|---|---|
| Model or provider change | Teams compare demos, skim logs, and make a judgment call under pressure. | Run baseline-versus-challenger evaluations and see pass/fail evidence before a change ships. |
| Cost and performance tradeoff | Savings, latency, and quality are discussed separately, usually without a shared source of truth. | Compare quality evidence with cost, runtime, and fallback options in the same qualification workflow. |
| Production approval | Prompts and model choices move through informal review or one-off scripts. | Only qualified prompt packages and runtime mappings are promoted for production use. |
| Incident readiness | Fallbacks are invented after prices change, providers fail, or behavior drifts. | Backup models, prompt mappings, and fallback policies are qualified before they are needed. |
Frequently Asked Questions
Is cost optimization just model shopping?
No. The important part is proving that a cheaper runtime still satisfies your actual objective criteria.
How do we avoid false savings?
ProofMap shows failure evidence alongside cost deltas so teams do not accept savings that create downstream support or review costs.
Who is this for?
Teams building AI agents or LLM-backed workflows that need evidence before changing prompts, models, providers, or fallback policies.
What does ProofMap produce?
A qualification trail: objective-bound evaluations, failure evidence, recommendations, and approved prompt or runtime mappings for production use.
Lower LLM cost safely
Benchmark lower-cost runtimes against the work your AI system actually does.
Start qualifying prompts