# 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:

## 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. (https://cutmyaispend.com/methods/cost-attribution-finops.md)
2. **Model routing & cascades** — Mini-class deployments for high-volume simple tasks; PTU (provisioned) capacity only for steady, latency-critical load. (https://cutmyaispend.com/methods/model-routing.md)
3. **Prompt caching** — Prefix caching discounts apply on Azure too — same stable-first prompt structure. (https://cutmyaispend.com/methods/prompt-caching.md)
4. **Batch APIs** — Azure’s Global Batch is ~50% off for async workloads. (https://cutmyaispend.com/methods/batch-apis.md)
5. **Output length control** — Structured outputs + max_tokens discipline — output tokens carry the same 3–8× premium here. (https://cutmyaispend.com/methods/output-length-control.md)

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

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Canonical: https://cutmyaispend.com/providers/azure-openai