AI cost governance: manage completed tasks, not token price

The signal and the facts that change the decision

Pressure to switch models by unit price can lower nominal cost while worsening retries, human review, latency, or completion rate. The governable cost is the accepted task within service and risk levels.

Price, cache, tools, context, output, retries, and review form the real unit economics. A failed task should include detection, correction, and impact cost, not only inference.

Averages hide tails; p95 and class-level distribution should accompany average cost. The cheapest model per token may consume more tokens, repeat calls, and transfer hidden cost to operations.

Decision, limits, and stop condition

Compare alternatives by total cost per accepted outcome, segmented by task and severity, including failures and human work.

The cheapest model per token may consume more tokens, repeat calls, and transfer hidden cost to operations.

Execution and proof of completion

Execution should start by defining the work unit and acceptance criterion per product. Then, instrumenting tokens, cache, calls, latency, retries, review, and failure. Closure requires reviewing outliers and routing or model decisions monthly.

The business case shows cost per accepted task, interval, volume, risk, and sensitivity to price and quality.

• defining the work unit and acceptance criterion per product.
• instrumenting tokens, cache, calls, latency, retries, review, and failure.
• reviewing outliers and routing or model decisions monthly.
• The business case shows cost per accepted task, interval, volume, risk, and sensitivity to price and quality.