Forecasting monthly spend

Multiplying a representative request by expected volume is the standard way to underestimate a feature, because a user action is a branching process rather than one call: guard classifiers, schema-validation retries, timeouts that bill for partial generations, rate-limit retries and user-initiated regenerates all add expected cost. This chapter builds the forecast as a request tree with measured branch probabilities, shows why the mean of a skewed cost distribution is the number to use, prices the batch tier and the adoption ramp, and separates a forecast from the controls that bound spend.

Token economics — read the current version at https://daviddominguez.dev/learn/en/token-economics/forecasting-monthly-spend

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