Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
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name
capacity
description
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
license
MIT
metadata
{"author":"Microsoft","version":"1.0.0"}
Capacity Discovery
Finds available Azure OpenAI model capacity across all accessible regions and projects. Recommends the best deployment location based on capacity requirements.
Quick Reference
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)
When to Use This Skill
✅ 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.
Scripts
Pre-built scripts handle the complex REST API calls and data processing. Use these instead of constructing commands manually.
Script
Purpose
Usage
scripts/discover_and_rank.ps1
Full discovery: capacity + projects + ranking
Primary script for capacity discovery
scripts/discover_and_rank.sh
Same as above (bash)
Primary script for capacity discovery
scripts/query_capacity.ps1
Raw capacity query (no project matching)
Quick capacity check or version listing
scripts/query_capacity.sh
Same as above (bash)
Quick capacity check or version listing
Workflow
Phase 1: Validate Prerequisites
az account show --query "{Subscription:name, SubscriptionId:id}" --output table
Phase 2: Identify Model and Version
Extract model name from user prompt. If version is unknown, query available versions:
💡 The script automatically queries capacity across ALL regions, cross-references with the user's existing projects, and outputs a ranked table sorted by: meets target → project count → available capacity.
Phase 3.5: Validate Subscription Quota
After discovery identifies candidate regions, validate that the user's subscription actually has available quota in each region. Model capacity (from Phase 3) shows what the platform can support, but subscription quota limits what this specific user can deploy.
# For each candidate region from discovery results:
$usageData = az cognitiveservices usage list --location <region> --subscription $SUBSCRIPTION_ID -o json 2>$null | ConvertFrom-Json
# Check quota for each SKU the model supports
# Quota names follow pattern: OpenAI.<SKU>.<model-name>
$usageEntry = $usageData | Where-Object { $_.name.value -eq "OpenAI.<SKU>.<model-name>" }
if ($usageEntry) {
$quotaAvailable = $usageEntry.limit - $usageEntry.currentValue
} else {
$quotaAvailable = 0 # No quota allocated
}
# For each candidate region from discovery results:
usage_json=$(az cognitiveservices usage list --location <region> --subscription "$SUBSCRIPTION_ID" -o json 2>/dev/null)
# Extract quota for specific SKU+model
quota_available=$(echo"$usage_json" | jq -r --arg name "OpenAI.<SKU>.<model-name>" \
'.[] | select(.name.value == $name) | .limit - .currentValue')
Annotate discovery results:
Add a "Quota Available" column to the ranked output from Phase 3:
Region
Available Capacity
Meets Target
Projects
Quota Available
eastus2
120K TPM
✅
3
✅ 80K
westus3
90K TPM
✅
1
❌ 0 (at limit)
swedencentral
100K TPM
✅
0
✅ 100K
Regions/SKUs where quotaAvailable = 0 should be marked with ❌ in the results. If no region has available quota, hand off to the quota skill for increase requests and troubleshooting.
Phase 4: Present Results and Hand Off
After the script outputs the ranked table (now annotated with quota info), present it to the user and ask:
🚀 Quick deploy to top recommendation with defaults → route to preset
⚙️ Custom deploy with version/SKU/capacity/RAI selection → route to customize
📊 Check another model or capacity target → re-run Phase 2
❌ Cancel
Phase 5: Confirm Project Before Deploying
Before handing off to preset or customize, always confirm the target project with the user. See the Project Selection rules in the parent router.
If the discovery table shows a sample project for the chosen region, suggest it as the default. Otherwise, query projects in that region and let the user pick.
Error Handling
Error
Cause
Resolution
"No capacity found"
Model not available or all at quota
Hand off to quota skill for increase requests and troubleshooting
Script auth error
az login expired
Re-run az login
Empty version list
Model not in region catalog
Try a different region: ./scripts/query_capacity.sh <model> "" eastus
"No projects found"
No AI Services resources
Guide to project/create skill or Azure Portal
Related Skills
preset — Quick deployment after capacity discovery
customize — Custom deployment after capacity discovery
quota — For quota viewing, increase requests, and troubleshooting quota errors, defer to this skill instead of duplicating guidance
