# AI pricing model comparison · [Product or AI capability] · [Date] ## Inputs - What the buyer values, and whether it can be measured and attributed: - Our cost per unit of work today, and its variability: - How predictable the bill must be (procurement's view): - Which budget pays: software / services / headcount: - Our confidence in consistent performance (eval results): - Can the customer audit the count? ## Comparison | Criterion | Seat | Usage | Credits | Outcome | Hybrid | |---|---|---|---|---|---| | Unit | | | | | | | Alignment with customer value | | | | | | | Tracks our cost | | | | | | | Bill predictability for the buyer | | | | | | | Revenue as agents replace work | | | | | | | Who carries performance risk | | | | | | | Who carries model-cost risk | | | | | | | Dispute risk | | | | | | | Metering we'd need to build | | | | | | | Gross margin at expected volume | | | | | | | Competitor and market norm | | | | | | ## Spend controls required for the chosen model Included allowance · alerts at [thresholds] · caps by [user, team, project] · overage policy (stop or finish and bill) · fallback behavior · rollover or expiry · burn-rate dashboard · plain-language unit names ## Recommendation Model: [ ]. Why: [ ]. What would make us change it: [ ]. Review date: [ ].