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How to cut your Azure OpenAI costs

Azure OpenAI adds a deployment-and-quota layer on top of OpenAI pricing, which creates its own waste (idle provisioned throughput) and its own levers:

The playbook, in order of leverage

  1. Cost attribution & AI FinOps — Split deployments per team/app and tag resources — the single shared deployment pattern makes chargeback impossible.
  2. Model routing & cascades — Mini-class deployments for high-volume simple tasks; PTU (provisioned) capacity only for steady, latency-critical load.
  3. Prompt caching — Prefix caching discounts apply on Azure too — same stable-first prompt structure.
  4. Batch APIs — Azure’s Global Batch is ~50% off for async workloads.
  5. Output length control — Structured outputs + max_tokens discipline — output tokens carry the same 3–8× premium here.

Pro tip

Audit PTU utilization monthly: idle provisioned throughput is the most common pure-waste line on Azure OpenAI bills.

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