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Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

Deploy Model to Optimal Region

Automates intelligent Azure OpenAI model deployment by checking capacity across regions and deploying to the best available option.

What This Skill Does

1. Verifies Azure authentication and project scope
2. Checks capacity in current project's region
3. If no capacity: analyzes all regions and shows available alternatives
4. Filters projects by selected region
5. Supports creating new projects if needed
6. Deploys model with GlobalStandard SKU
7. Monitors deployment progress

Prerequisites

• Azure CLI installed and configured
• Active Azure subscription with Cognitive Services read/create permissions
• Azure AI Foundry project resource ID (PROJECT_RESOURCE_ID env var or provided interactively)
  • Format: /subscriptions/{sub-id}/resourceGroups/{rg}/providers/Microsoft.CognitiveServices/accounts/{account}/projects/{project}
  • Found in: Azure AI Foundry portal → Project → Overview → Resource ID

Quick Workflow

Fast Path (Current Region Has Capacity)

1. Check authentication → 2. Get project → 3. Check current region capacity
→ 4. Deploy immediately

Alternative Region Path (No Capacity)

1. Check authentication → 2. Get project → 3. Check current region (no capacity)
→ 4. Query all regions → 5. Show alternatives → 6. Select region + project
→ 7. Deploy

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Deployment Phases

Phase	Action	Key Commands
1. Verify Auth	Check Azure CLI login and subscription	az account show, az login
2. Get Project	Parse PROJECT_RESOURCE_ID ARM ID, verify exists	az cognitiveservices account show
3. Get Model	List available models, user selects model + version	az cognitiveservices account list-models
4. Check Current Region	Query capacity using GlobalStandard SKU	az rest --method GET .../modelCapacities
5. Multi-Region Query	If no local capacity, query all regions	Same capacity API without location filter
6. Select Region + Project	User picks region; find or create project	az cognitiveservices account list, az cognitiveservices account create
7. Deploy	Generate unique name, calculate capacity (50% available, min 50 TPM), create deployment	az cognitiveservices account deployment create

For detailed step-by-step instructions, see workflow reference.

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Error Handling

Error	Symptom	Resolution
Auth failure	az account show returns error	Run az login then az account set --subscription <id>
No quota	All regions show 0 capacity	Defer to the quota skill for increase requests and troubleshooting; check existing deployments; try alternative models
Model not found	Empty capacity list	Verify model name with az cognitiveservices account list-models; check case sensitivity
Name conflict	"deployment already exists"	Append suffix to deployment name (handled automatically by generate_deployment_name script)
Region unavailable	Region doesn't support model	Select a different region from the available list
Permission denied	"Forbidden" or "Unauthorized"	Verify Cognitive Services Contributor role: az role assignment list --assignee <user>

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Advanced Usage

# Custom capacity
az cognitiveservices account deployment create ... --sku-capacity <value>

# Check deployment status
az cognitiveservices account deployment show --name <acct> --resource-group <rg> --deployment-name <name> --query "{Status:properties.provisioningState}"

# Delete deployment
az cognitiveservices account deployment delete --name <acct> --resource-group <rg> --deployment-name <name>

Notes

• SKU: GlobalStandard only — API Version: 2024-10-01 (GA stable)

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Related Skills

• microsoft-foundry - Parent skill for Azure AI Foundry operations
• quota — For quota viewing, increase requests, and troubleshooting quota errors, defer to this skill
• azure-quick-review - Review Azure resources for compliance
• azure-cost-estimation - Estimate costs for Azure deployments
• azure-validate - Validate Azure infrastructure before deployment