Inference Efficiency Ratio: A Simple Metric to Track AI Feature Profitability

Many AI-powered products appear successful by usage metrics while quietly losing money on every user action, with teams unable to identify which workflows drain margins. The Inference Efficiency Ratio (IER) addresses this by dividing AI-attributed product revenue by production inference cost, giving builders a clear unit-economics signal before scaling. An IER of 5:1, for example, means a workflow returns five dollars in product revenue for every dollar spent on model execution. Unlike token-level cost tracking, IER accounts for retries, failed runs, embedding calls, tool-call overhead, and other hidden expenses that affect true profitability. The metric is most useful when tracked by product line, tenant tier, and model route, and should be monitored alongside quality metrics rather than in isolation.