You must ensure an Azure OpenAI-powered application never exceeds a defined token-per-minute budget across all...

Configure deployment-level Tokens-per-Minute (TPM) and Requests-per-Minute (RPM) rate limits for the Azure OpenAI model deployment. Azure OpenAI quota is allocated to individual model deployments in TPM units, and Microsoft documents that the TPM assigned to a deployment directly maps to the rate limit enforced for inference requests against that deployment. An associated RPM limit is also enforced according to the model ' s TPM-to-RPM ratio.

Because all application users invoke the same deployment, this enforcement occurs at the deployment boundary rather than independently for each end user. Once request traffic would exceed the configured capacity, Azure applies throttling rather than allowing unrestricted token consumption. Microsoft Foundry additionally supports explicit token-management controls through AI Gateway, where a TPM limit can be set for a model deployment; requests exceeding the limit receive HTTP 429 Too Many Requests responses.

Content filtering governs safety categories, not consumption. An Azure AI Search vector-index limit controls search storage rather than generated tokens. Temperature changes sampling randomness and has no enforcement role in rate or cost control.

Study Guide references: Azure OpenAI quota management; TPM/RPM rate limits; deployment capacity; throttling; Foundry AI Gateway token management.

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