Put Guardrails Around LLM Spend
LLM budgets drift when teams lack visibility into which work truly needs premium models. ProofMap helps define safe, qualified cost controls.
Get StartedWhy Choose ProofMap
Qualify cheaper routes
Move eligible tasks to lower-cost models only after they pass objective tests.
Find expensive failure loops
Spot retries, manual review triggers, and fallback paths that erase expected savings.
Create defensible policies
Tie budget rules to evaluation evidence instead of arbitrary caps.
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
What is an LLM budget guardrail?
A rule that controls model selection, prompt shape, or fallback behavior while preserving the required quality bar.
Can budget rules be task-specific?
Yes. ProofMap helps qualify different mappings for different objectives or criteria.
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.