AI API cost control starts before traffic increases. The smallest useful habit is a review loop that connects prompt shape, model route, expected volume, and actual token usage.
Separate workload classes
Do not treat all model calls as one budget. Split user-facing requests, batch jobs, content review, and experiments into separate buckets with separate owners.
Estimate before the first run
For each workload, estimate average input tokens, output tokens, request count, and failure retry rate. The point is not perfect forecasting; it is making assumptions visible before volume hides them.
Review actual usage
After the first run, compare estimated and actual token usage. If the gap is large, adjust prompt length, chunking, model choice, or scheduling before raising the traffic limit.
Keep scaling gated
Scaling should require a recent usage snapshot and an owner decision. This prevents small prompt changes from silently becoming recurring spend.