deploy-model

To deploy Azure OpenAI models with optimal quota, this skill performs capacity discovery across regions, compares availability, and routes the request to the best available region automatically. It validates live quota and model capacity before proceeding with the deployment.

Finding available PTU capacity for gpt-4o is handled through the capacity discovery workflow, which compares regions and projects to locate where the model can be deployed. It then hands off to the appropriate deployment flow.

Yes, Azure CLI authentication is required to deploy models in Azure AI Foundry using this skill. The deterministic scripts rely on authenticated access to validate live quota, check model capacity, and execute the deployment safely.

Yes, you can select a custom SKU, exact model version, capacity, and content filter for Azure OpenAI deployment. The custom deployment workflow also supports advanced options like dynamic quota, priority processing, and spillover configuration.

Capacity discovery for Anthropic or MaaS models in Azure AI Foundry works by routing deployment requests to the correct workflow without manual branching. It validates available quota and model capacity before confirming the target project and proceeding safely.

Azure OpenAI model deployment fails due to regional capacity limits when the target region is full. This skill prevents failure by validating live quota beforehand and providing automatic fallback to the best available region during preset deployments.