Many production Azure OpenAI workloads—such as chatbots, inference services, and retrieval-augmented generation (RAG) pipelines—use PTUs consistently throughout the day. When usage stabilizes after initial experimentation, continuing to rely on on-demand PTUs results in ongoing unnecessary spend. These workloads are strong candidates for reserved PTUs, which provide identical performance guarantees at a substantially reduced hourly rate. Migrating to reservations usually requires no architectural changes and delivers immediate cost savings.
PTUs are billed hourly based on provisioned throughput. On-demand PTUs use standard hourly rates, whereas reserved PTUs offer significant discounts—often up to \~80%—when capacity is committed for a month or year. Workloads running continuously on on-demand PTUs incur avoidable premium pricing.
