Choose Cheaper Models Without Breaking Production
Cheaper models are attractive until they miss an edge case. ProofMap gives teams the evidence to switch where safe and hold the line where needed.
Get StartedWhy Choose ProofMap
Compare against the baseline
Evaluate challengers side by side with the model already serving production.
Separate safe from risky work
Identify criteria that pass, criteria that fail, and criteria that need model-specific prompt adjustments.
Defend the decision
Use qualification reports to explain why a model was promoted, rejected, or limited to fallback coverage.
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
When is a cheaper model safe to use?
When it passes the objective criteria that matter for the workflow, with evidence you can inspect and repeat.
What if the cheaper model is only good for simple cases?
ProofMap supports partial qualification and fallback mappings, so simple cases can move while harder cases stay on the baseline.
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.
Switch with confidence
Prove the cheaper model before it touches production traffic.
Start qualifying prompts