capacity

Discover Azure OpenAI model capacity across regions and rank deployment options.

12|1|Updated Mar 15, 2026
One-click install
npx skills add https://github.com/jorgeasaurus/agent-skills --skill capacity-jorgeasaurus
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: capacity
Source: https://github.com/jorgeasaurus/agent-skills/tree/main/capacity
Command: npx skills add https://github.com/jorgeasaurus/agent-skills --skill capacity-jorgeasaurus

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires az, and includes scripts (resource) components.

What problem does it solve?

This Skill helps users identify the available capacity of Azure OpenAI models across different regions, ensuring they can choose the best deployment location based on their capacity requirements.

Core Features & Use Cases

  • Capacity Discovery: Provides a ranked table of regions with available capacity for a specified model and version.
  • Quota Analysis: Checks the user's subscription quota in each region for the model.
  • Quick Deployment Handoff: After discovery, users can quickly deploy to the recommended region or customize their deployment.
  • Use Case: When a user needs to deploy a new Azure OpenAI model but is unsure about the available capacity and best region for deployment.

Quick Start

Use the capacity skill to find the best region for deploying the 'gpt-4o' model with a minimum capacity of 10K TPM.

Frequently Asked Questions about capacity

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I find Azure OpenAI model capacity across different regions?

Azure OpenAI capacity discovery involves querying the REST API to check subscription quotas across regions. It presents a ranked table of regions with available capacity for a specified model and version to recommend optimal deployment locations.

Do I need Azure CLI authentication to check Azure OpenAI quota?

Yes, Azure CLI authentication is required to check Azure OpenAI quota. The capacity discovery process handles REST API calls using the 'az' dependency to authenticate your subscription and retrieve region quota data.

What is the best way to compare Azure OpenAI deployment quotas by region?

The best way to compare Azure OpenAI deployment quotas is by analyzing a ranked table of regions. This approach evaluates subscription quotas across regions for a specific model version to recommend the optimal deployment location.

How do I deploy an Azure OpenAI model to a region with available capacity?

To deploy an Azure OpenAI model, first discover the optimal region using quota analysis. After capacity discovery, you can quickly deploy to the recommended region or customize your deployment settings based on the ranked results.

Why does my Azure OpenAI deployment fail due to quota limits in specific regions?

Azure OpenAI deployments fail due to quota limits when a region lacks the required capacity for your specified model and version. Quota analysis checks your subscription limits in each region to prevent this and recommend locations with sufficient capacity.