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1. Cost forecasting is an operating requirement

Token-based systems become expensive when usage patterns change faster than the team notices. Forecasting provides a way to estimate monthly exposure before a new prompt, routing rule, or product feature changes the bill.

2. Build the forecast from traffic and token drivers

A practical model includes request volume, average input tokens, average output tokens, model routing mix, cache hit rate, retry rate, and fallback frequency. These are the levers that actually move cost.

3. Separate steady usage from spike scenarios

Teams should model baseline weeks and event-driven spikes separately. Product launches, support incidents, or sudden growth in long conversations can distort cost quickly if the forecast assumes only stable traffic.

4. Forecasts are most useful when tied to alarms

A forecast should feed alert thresholds, not just a finance spreadsheet. That means defining budget warning levels, hard-stop conditions, and routing adjustments before usage crosses the limit.

5. Review forecast error every cycle

The forecast becomes trustworthy only when the team compares estimate versus actual, explains the gap, and updates the model. That review loop prevents cost planning from becoming a static document.