AI Credit Infrastructure for LLM Products: From Design to Scale

AI credits, increasingly used in AI products, present a complex infrastructure challenge rather than merely a data model issue, necessitating real-time deduction, shared pools, and low-latency enforcement for effective implementation. As AI products evolved, the complexity of managing individual feature pricing led to the adoption of a unified credit system, where one currency is consumed across multiple features, such as Stability AI's use of credits across various capabilities. The choice between subscription-based and prepaid credit models impacts billing, enforcement, and system complexity, with each model suiting different usage predictability and customer commitment levels. Essential components of a credit system include credit wallets, grants, consumption rates, depletion behavior, and rollover policies, all requiring precise engineering to ensure consistent real-time enforcement and financial alignment. Credits must be tracked meticulously, incorporating effective and expiration dates, cost basis, and burn priority, to maintain financial auditability and avoid issues like double deductions or revenue leakage. The infrastructure must support dynamic burn order recalculations and real-time enforcement across multiple features, aligning product behavior with billing and financial records. Comprehensive observability and customer-facing visibility are crucial to maintaining trust and operational reliability, with tools needed for audit logs, grant tracking, and manual overrides. Building such a system from scratch can become an ongoing maintenance burden, prompting some companies to seek specialized solutions like Stigg, which offers a runtime layer for credit management, ensuring product teams can focus on development rather than billing logic.

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