COST INTELLIGENCE SOLUTION
AI cost intelligence
AI cost intelligence needs more than a model-price table. Attribute input, output, cache, requests and agent behavior to applications, users, customers and features, then evaluate savings against quality and reliability risk.
Content reviewed: October 7, 2026 · Methodology
Metrics that should answer a decision
- model
- input / output / cached tokens
- requests
- application / feature
- user / customer
- AI agent
Questions CostNerve should answer
Which model, prompt or agent created the spend change?
The answer should be backed by provider evidence, visible attribution and an estimate of economic impact.
How much does one successful request, agent run or feature action cost?
The answer should be backed by provider evidence, visible attribution and an estimate of economic impact.
How much of the bill is input, output, cache or retries?
The answer should be backed by provider evidence, visible attribution and an estimate of economic impact.
Where is a model substitution economically attractive without hiding quality risk?
The answer should be backed by provider evidence, visible attribution and an estimate of economic impact.
Spend → Explain → Forecast → Detect → Recommend → Save
The interface should show the answer first and technical detail second. A startup should not need to operate an enterprise FinOps program just to understand its margin.