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iinoshirozheng
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capacity

Discover Azure OpenAI model capacity across regions and projects.

Updated Aug 23, 2026
Search Tags:#deployment#discovery#quota#ranking#capacity#region#azure-openai
One-click install
npx skills add https://github.com/iinoshirozheng/SinoCloudChat --skill capacity-iinoshirozheng
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: capacity
Source: https://github.com/iinoshirozheng/SinoCloudChat/tree/main/.agents/skills/microsoft-foundry/models/deploy-model/capacity
Command: npx skills add https://github.com/iinoshirozheng/SinoCloudChat --skill capacity-iinoshirozheng

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires az, jq, python3, and includes scripts (resource) components.

What problem does it solve?

Organizations planning Azure OpenAI deployments face scattered capacity data, regional availability, and quota constraints that slow decision-making. This skill centralizes capacity visibility across regions and projects to enable informed deployment choices.

Core Features & Use Cases

  • Capacity discovery across Azure OpenAI regions to surface available TPM capacity.
  • Cross-reference with existing AI Projects and per-region quotas to rank deployment options.
  • Use cases include identifying the best region for a given model and TPM target, or diagnosing capacity gaps before deployment.

Quick Start

Run the capacity discovery workflow to identify regions with available Azure OpenAI capacity.

Frequently Asked Questions about capacity

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

FAQPage Schema
How do I check Azure OpenAI capacity and quotas across multiple regions?▼

You can discover Azure OpenAI capacity across regions by running prebuilt scripts that query per-region quotas and existing AI projects, producing a ranked table of available TPM capacity to guide deployment decisions.

What is the best way to find available TPM capacity for a specific Azure OpenAI model?▼

Finding available TPM capacity involves cross-referencing regional availability with existing AI projects and per-region quotas, which generates a ranked view of deployment options for your specific Azure OpenAI model.

Do I need Azure CLI and Python installed to discover Azure OpenAI regional quotas?▼

Yes, discovering Azure OpenAI regional quotas requires Azure CLI, Python3, and jq installed, as the prebuilt scripts depend on these tools to query capacities, projects, and quotas across regions.

How does Azure OpenAI region ranking work for capacity planning?▼

Azure OpenAI region ranking for capacity planning works by querying available TPM capacity and per-region quotas, then structuring the results into a ranked table that surfaces optimal regions for your deployment targets.

Can I diagnose Azure OpenAI quota constraints before deploying a model?▼

Yes, you can diagnose Azure OpenAI quota constraints before deployment by running capacity discovery scripts to identify capacity gaps and surface available TPM capacity across regions and projects.

Why are my Azure OpenAI deployments failing due to regional capacity limits?▼

Azure OpenAI deployments often fail due to regional capacity limits when the target region lacks sufficient TPM quota, which you can troubleshoot by querying per-region quotas and ranking alternative deployment options.

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