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capacity

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

2.9k|323|Updated Jan 16, 2026
Search Tags:#deployment#quota#capacity#region#azure-openai#ai-projects
One-click install
npx skills add https://github.com/microsoft/skills --skill capacity-microsoft
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: capacity
Source: https://github.com/microsoft/skills/tree/main/.github/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model/capacity
Command: npx skills add https://github.com/microsoft/skills --skill capacity-microsoft

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Azure OpenAI deployments often struggle with capacity visibility across regions and projects. This Skill discovers available capacity across regions and helps identify the best deployment locations based on capacity, project availability, and quotas.

Core Features & Use Cases

  • Discover capacity per region for a given model and version.
  • Cross-reference with existing AI Foundry projects and per-region quotas to surface viable deployment locations.
  • Output a ranked, hand-off-ready summary to facilitate quick deployment planning and handoff to preset or customize steps.

Quick Start

Run the capacity discovery for your model to identify regions with available capacity and ready-to-use deployment options.

Frequently Asked Questions about capacity

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

FAQPage Schema
How do I discover Azure OpenAI capacity across regions for a specific model?▼

Azure OpenAI capacity discovery involves analyzing all regions and existing AI Foundry projects to surface viable deployment locations based on quota availability. It outputs a ranked summary of deployment options ready for handoff to preset or custom deployment steps.

Why does my Azure OpenAI deployment fail due to region capacity constraints?▼

Azure OpenAI deployments fail when selected regions lack available capacity or sufficient quotas for the target model. This Skill cross-references per-region quotas and AI Foundry project availability to surface only locations with viable deployment options.

What's the best way to rank Azure OpenAI deployment options by quota and region?▼

The best way to rank Azure OpenAI deployment options is to query capacity and check quotas across all regions and AI Foundry projects. This produces a ranked summary of viable deployment locations ready for immediate handoff.

Do I need az and jq installed to check Azure OpenAI capacity per region?▼

Yes, you need az, jq, and python3 installed to check Azure OpenAI capacity per region. These dependencies support the scripts required to query capacity, analyze quotas, and output the ranked deployment summary.

Can I cross-reference AI Foundry projects with Azure OpenAI quotas to find viable deployment locations?▼

Yes, you can cross-reference existing AI Foundry projects with per-region Azure OpenAI quotas to find viable deployment locations. This Skill analyzes project availability and capacity to output a ranked summary of ready-to-use deployment options.

How does the capacity discovery process handle Azure OpenAI deployments across multiple projects?▼

The capacity discovery process handles Azure OpenAI deployments by analyzing all regions and AI Foundry projects to identify available capacity. It checks quotas across these projects and outputs a ranked summary of deployment locations for handoff.

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