Do Tuan Anh

Enterprises buy LLM inference through three structurally different pricing models: credit-based subscriptions, pay-per-token APIs, and self-hosted GPU capacity, with little guidance on which to use at a given volume or what signals it's time to switch. This talk introduces the Efficiency Curve: cost per token under the cheapest procurement mode at each volume, and its crossover points. Using a July-2026 pricing snapshot across three model quality tiers, I show a counter-intuitive finding: the subscription-to-API crossover often never economically materializes; a credit plan's idealized per-token rate can undercut every API price at scale, so the real boundary is a terms-of-service seat limit, not economics. The self-hosting crossover, by contrast, is a genuine price crossover, landing between roughly 360-899M tokens/month depending on tier, and it shifts with output-token share. I walk through the derivation and share a monitoring framework that ties each metric to the procurement decision it should inform, drawn from running AI infrastructure inside a regulated bank.