AI Usage Control: Tokens, Credits & Real-Time Enforcement

AI usage control is runtime infrastructure that evaluates entitlements, token allowances, credit balances, and other consumption limits before AI workloads such as model calls, tools, or agent workflows begin, distinguishing it from metering, which records usage after execution. Effective systems combine metering, entitlement resolution, ledger-backed credits, enforcement, and billing while supporting outcomes such as approval, hard limits, controlled overages, and upgrade prompts. Token limits are suited to direct model consumption, whereas credits provide a shared commercial unit for mixed workloads; robust credit systems require expiry tracking, burn priorities, separate paid and promotional balances, append-only ledgers, and reconciliation. The text emphasizes the importance of atomic debits, idempotency, tenant hierarchies, backend feature gating, cache and timeout policies, and recovery procedures to prevent overspending and inconsistent state under concurrent traffic or service failures. It argues that internally built solutions can become difficult to maintain as plans, trials, overrides, shared balances, and package changes grow more complex, and presents Stigg’s sidecar and Edge API architecture as a modular option for enforcing product-facing usage rules while existing billing systems continue handling invoices and payments.

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