capacity

Discover Azure OpenAI model capacity across regions and projects with quota validation.

1|Updated Apr 4, 2026

Search Tags:#deployment #azure #discovery #quota #openai #capacity #region-selection

Ready to run

npx skills add https://github.com/p-potvin/vaultwares-pipelines --skill capacity-p-potvin

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: capacity
Source: https://github.com/p-potvin/vaultwares-pipelines/tree/main/.github/agents/agents/microsoft-foundry/models/deploy-model/capacity
Command: npx skills add https://github.com/p-potvin/vaultwares-pipelines --skill capacity-p-potvin

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill discovers available Azure OpenAI model capacity across the user's accessible subscriptions, regions, and projects, solving the problem of where a specified model and capacity target can actually be deployed.

Core Features & Use Cases

Cross-region capacity discovery: Scans capacity and model versions across all accessible regions to locate deployable resources.
Quota-aware ranking: Compares available capacity, project counts, and subscription quotas to produce a ranked list of candidate regions and projects.
Operational hand-off: Produces read-only recommendations and routes the user to preset or customize deployment skills; ideal for finding alternatives after quota errors or selecting the best region for models like gpt-4o.

Quick Start

Use the capacity skill to find regions and projects that can deploy gpt-4o with at least 10000 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 regions when deployment fails due to quota errors?▼

Azure OpenAI capacity discovery scans accessible subscriptions and regions to validate quotas and rank deployable locations. It analyzes available capacity, model versions, SKUs, and project counts to recommend alternative deployment regions after quota failures.

What's the best way to check Azure OpenAI quota and available capacity before deploying models like gpt-4o?▼

Use Azure OpenAI capacity discovery to perform read-only quota validation across all accessible subscriptions and regions. It ranks candidate regions by comparing available capacity, project counts, and subscription quotas to identify optimal deployment locations for specific models.

Do I need Azure CLI authentication to check Azure OpenAI deployment capacity across subscriptions?▼

Yes, Azure CLI authentication is required to perform read-only capacity discovery across subscriptions. The Skill uses authenticated access to scan quotas, SKUs, and project resources across all accessible regions and produce ranked deployment recommendations.

Can I search multiple Azure subscriptions simultaneously to find regions with available OpenAI model capacity?▼

Cross-region capacity discovery scans all accessible Azure subscriptions and regions simultaneously to locate deployable resources. It compares available capacity, model versions, and subscription quotas across projects to produce a ranked list of candidate deployment locations.

How does quota-aware ranking work when selecting Azure OpenAI deployment regions?▼

Quota-aware ranking compares available capacity, project counts, and subscription quotas across accessible Azure regions. It validates quota limits against deployment targets and ranks candidate regions and projects by their ability to support specified models and capacity requirements.

What happens after I find candidate Azure OpenAI deployment regions using capacity discovery?▼

Capacity discovery produces read-only recommendations and routes you to preset or customizable deployment skills. The operational hand-off provides ranked candidate regions and projects, enabling immediate deployment of models like gpt-4o with specified TPM targets.