AI Models tool

Prompt routing savings estimator

Estimate whether routing prompts by difficulty will save money after eval and monitoring costs.

Fast answerA savings case with prompt classes, fallback rules, and the minimum eval evidence needed before rollout.

Inputs

What to collect

Prompt classes	Support reply, extraction, research, code review, agent plan, or customer-facing answer.
Failure cost	Rework time, refund risk, incident risk, or human review cost.
Fallback rules	When the workflow should retry, escalate, or ask a human.

Method

How to use it

1	Tag existing prompts by job and risk.
2	Run a paired test across premium and utility models.
3	Keep the cheaper model only where failures do not erase savings.

Output

How to read the result

Good candidate	High-volume easy prompts	Extraction, classification, and simple drafts usually route well.
Poor candidate	Low-volume expert prompts	Complex planning and code review often deserve the stronger lane.
Safety net	Escalation trigger	Escalate on low confidence, policy risk, missing evidence, or repeated retries.

Thresholds

Useful vs risky

Healthy	Routing saves at least 20% after retry and review costs.
Risky	Savings are calculated from list prices without measuring task success.

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