capacity

To check Azure OpenAI model capacity across regions, run the capacity skill with a target model name, version, and minimum capacity requirement to generate a ranked table of available deployment locations.

The best way to find available quota limits for Azure OpenAI deployments is using a capacity discovery tool that analyzes accessible regions and projects, then recommends optimal deployment locations based on your capacity requirements.

Yes, you need Azure CLI installed and API access to Cognitive Services to analyze OpenAI deployment capacity, as the skill requires these dependencies to discover available quota limits across regions.

Yes, you can compare Azure OpenAI capacity availability across multiple projects by running the capacity skill, which discovers and analyzes quota limits across all accessible regions and projects to recommend optimal deployment locations.

Azure OpenAI deployments fail due to insufficient capacity when a region's quota limits are reached, which you can prevent by using capacity planning analysis to discover regions with available model capacity before deployment.

The capacity skill works with Azure OpenAI models by accepting a specified model name and version as input, then scanning accessible regions to find available capacity that meets your minimum deployment target.