Usage Management for AI Products: Limits, Credits & Enforcement

Usage management for AI products extends usage metering by translating consumption data for tokens, API calls, compute, storage, and agent actions into runtime decisions about limits, credits, entitlements, budgets, permissions, and overage behavior. It is particularly important for AI systems because a single user action may trigger multiple costly model, retrieval, tool, and external API operations, often with several agents drawing concurrently from shared organizational, team, or agent-level balances. While batch updates can support reporting and reconciliation, real-time state is needed when requests must be blocked, approved, billed as overages, or funded through top-ups before they proceed, especially to avoid overspending during concurrent activity. Effective implementations must address changing plans, multi-level tenancy, auditable credit histories, service failures, and consistent enforcement across active workloads. The text presents Stigg as a usage-management runtime that provides metering, credits, entitlements, ledger-backed balances, multi-level tenancy, synchronous enforcement, local sidecar checks, and integrations with billing systems such as Stripe and Zuora.

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