capacity

Topics in the source

• Capacity Discovery
• Quick Reference
• When to Use This Skill
• Scripts
• Workflow
• Phase 1: Validate Prerequisites
• Phase 2: Identify Model and Version
• Phase 3: Run Discovery

Finds available Azure OpenAI model capacity across all accessible regions and projects. Recommends the best deployment location based on capacity requirements. | Property | Description | |----------|-------------| | Purpose | Find where you can deploy a model with sufficient capacity | | Scope | All regions and projects the user has access to | | Output | Ranked table of regions/projects with available capacity | | Action | Read-only analysis — does NOT deploy. Hands off to preset or customize | | Authentication | Azure CLI (az login) | ✅ User asks "where can I deploy gpt-4o?" ✅ User specifies a capacity target: "find a region with 10K TPM for gpt-4o" ✅ User wants to compare availability: "which regions have gpt-4o available?" ✅ User got a quota error and needs to find an alternative location ✅ User asks "best region and project for deploying model X" After discovery → hand off to preset or customize for actual deployment. Pre-built scripts handle the complex REST API calls and data processing. Use these instead of constructing commands manually. | Script | Purpose | Usage | |--------|---------|-------| | scripts/discoverandrank.ps1 | Full discovery: capacity + projects + ranking |

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