Evaluation spend belongs in the AI budget
August 27, 2026
Evaluation spend is part of the cost of operating AI, not a free engineering sidecar. Regression suites, red-team tests, model comparisons, and release certification consume tokens, tools, and reviewer time. Put them in the AI budget and judge them by the decisions they improve.
Separate evaluation types
Budget pull-request checks, nightly regressions, pre-release certification, and exploratory research separately. They have different urgency, coverage, and acceptable cost. A single blended number makes an expensive experiment look like production demand.
Measure confidence per dollar
Track test-set coverage, quality confidence, cost per run, cost per detected defect, and cost per release decision. Cache stable fixtures and sample redundant cases, but never hide evaluation cost inside production feature spend.
Make it a portfolio decision
Increase evaluation spend when the downside of a bad release is high. Reduce or redesign a suite when it produces little new evidence. Finance can ask which decision the next run will change and what quality or financial risk it retires.
The right evaluation budget is not the smallest one. It is the amount that buys credible release confidence at a known cost.
Related
- Proving AI ROI
- AI spend forecasting
- AI governance framework