Inference gets 10x cheaper every year. Your price sheet updates once.

Rapidly falling AI inference costs and rising, agent-driven compute usage make static pricing increasingly unreliable, creating risks of being undercut when costs decline or losing money when customer usage exceeds fixed-price assumptions. Because a single user request can trigger many model calls, tool invocations, validations, and retries, traditional request-based SaaS pricing no longer accurately reflects the work being performed. The passage argues that companies need repricing speed as an operational capability, achieved by moving pricing logic out of application code and into a configurable entitlement system that can apply customer-specific plans, budgets, and limits at runtime. However, flexible pricing also requires trustworthy real-time metering, a customer-facing billing unit abstracted from provider token prices, and request-path enforcement that can stop usage before budgets are exceeded. Building this infrastructure is technically demanding because it must accurately track high-volume fan-out workloads, reconcile usage for finance, maintain low p99 latency, and prevent concurrent requests from overspending shared balances.

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