01 / Find repeated, bounded judgments
List the steps in your workflow. Highlight operations whose answer is a label, a yes/no judgment or a defined rating. Those steps are candidates for a decision-model experiment. Keep the baseline workflow so you can detect when adding a classifier merely adds another bill.
02 / Count the whole path
Measure the decision stage and every step that follows it, including retries and fallback to another model. For local inference, record the hardware cost and how much work the service completes at realistic utilization. Downloadable weights do not imply zero operating cost.
cost per accepted result =
(decision calls + downstream model calls + retries
+ allocated serving cost + measured review cost)
/ results that meet the same acceptance criteria
Track latency, error rate and review frequency alongside cost.03 / Run a controlled comparison
Use a representative labelled workload, the same acceptance criteria and a fixed time window. Compare the baseline with the proposed pipeline. Keep model versions, request sizes and concurrency in the report. Report failed and deferred requests instead of dropping them from the denominator.
For planning, vary request volume and fallback rate. A low-volume service may not use a dedicated GPU efficiently; a frequent fallback may consume both the decision call and the original generation call. Those are scenarios to measure, not universal reasons to choose either deployment path.
04 / Publish the evidence behind the saving
A useful result states the task, sample size, quality threshold, serving setup and observed cost before and after the change. Keep API list prices separate from measured cost per thousand decisions. This catalog currently supplies sourced capability notes and pricing where verified; it has no measured savings claim.