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capacity

Identify Azure regions with sufficient OpenAI model capacity across projects.

Updated Apr 10, 2026
Search Tags:#deployment#azure#discovery#quota#openai#capacity#region
One-click install
npx skills add https://github.com/attentiondotnet/azure-skills --skill capacity-attentiondotnet
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: capacity
Source: https://github.com/attentiondotnet/azure-skills/tree/main/.github/plugins/azure-skills/skills/microsoft-foundry/models/deploy-model/capacity
Command: npx skills add https://github.com/attentiondotnet/azure-skills --skill capacity-attentiondotnet

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Capacity Discovery identifies Azure regions with sufficient OpenAI model capacity across all accessible projects, enabling teams to choose deployment locations with confidence and speed.

Core Features & Use Cases

  • Discover capacity across all regions for a given OpenAI model and version
  • Rank regions by available capacity and cross-check with existing AI projects
  • Validate per-region quotas and surface deployment feasibility
  • Hand off to preset or customize deployment workflows with clear region recommendations

Quick Start

Run the discovery script with your model name and version to obtain a ranked region list for deployment.

Frequently Asked Questions about capacity

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

FAQPage Schema
How do I find Azure regions with available OpenAI capacity for a specific model?▼

This capacity discovery script identifies Azure regions with sufficient OpenAI model capacity by analyzing quotas and existing projects across all accessible subscriptions, then outputs a ranked list of deployment options.

What is the best way to check Azure OpenAI quotas across multiple projects before deployment?▼

A capacity discovery script validates per-region quotas and cross-references them with existing Azure OpenAI projects to surface feasible deployment locations, providing a structured ranked list of regions.

Does this Azure OpenAI capacity discovery workflow require Azure CLI?▼

Yes, this capacity discovery workflow requires the Azure CLI (az) to query accessible projects, along with jq and python3 to process and analyze regional quotas.

How do I rank Azure OpenAI regions by available capacity for deployment?▼

You rank Azure OpenAI regions by running a discovery script that evaluates model-specific quotas across all regions and projects, generating a structured ranked list of deployment options.

Can I hand off Azure OpenAI region recommendations directly to a deployment workflow?▼

Yes, the capacity discovery output is structured for direct hand-off to preset or customize deployment workflows, providing clear region recommendations and feasibility notes based on quota analysis.

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