customize

To deploy Azure OpenAI models with custom SKU and capacity, you can use an interactive guided process that steps through model selection, SKU configuration, and capacity allocation to ensure precise deployment control.

Yes, you can apply custom RAI policies when deploying models on Azure AI Foundry. The deployment process allows you to select specific Responsible AI policies alongside model versions and capacity settings.

Deploying a customized Azure OpenAI model requires the Azure CLI and an existing Azure AI Foundry project setup. You must have these prerequisites configured before initiating the interactive deployment.

Azure OpenAI deployment supports dynamic quota and priority processing as advanced options. These features allow you to optimize model availability and processing speed for high-traffic production applications.

The best way to configure a high-traffic production deployment for Azure OpenAI is using an interactive customization process, allowing precise control over model version, SKU, capacity, and advanced dynamic quota settings.

Customizing Azure OpenAI model deployments allows you to select specific model versions, SKUs, capacities, and RAI policies, ensuring the configuration precisely meets the demands of your production application.