Tiered Pricing: How It Works and Why Enforcement Matters

Implementing tiered pricing for AI products involves defining plans with specific features, usage limits, and pricing that scales with increased consumption, but the real challenge lies in the enforcement layer, which ensures requests adhere to these rules before execution. This model is particularly suited for AI products due to varying serving costs based on usage, where a customer with higher model calls incurs significantly more costs. Unlike traditional SaaS, AI tiered pricing includes runtime enforcement of credits, model access, and token limits, requiring checks before requests are fulfilled. Enforcement issues, such as cost leakage and access control failures, occur when these checks are not properly synchronized with billing, turning pricing rules into infrastructure concerns. Effective enforcement requires a robust architecture with a product catalog as the source of truth, synchronously evaluated constraints, and a credit ledger to manage concurrent usage. Solutions like Stigg's approach emphasize the need for an independent enforcement layer that operates within the infrastructure to maintain efficiency and accuracy, separate from billing systems, ensuring that pricing changes can be implemented without extensive engineering modifications.

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