
Customize Model Deployment
OfficialFreeDeploy Azure OpenAI models with full customization control.
Free · Opens the source repo
What Customize Model Deployment does
The Customize Model Deployment skill provides an interactive guided workflow for deploying Azure OpenAI models, allowing users to exercise full control over various deployment parameters. This skill is particularly useful for developers and data scientists who require specific configurations when deploying models, such as selecting the model version, SKU type, and capacity. The step-by-step process ensures that users can make informed decisions at each stage of the deployment, tailoring the setup to meet precise requirements.
Users begin the deployment process by verifying their authentication and retrieving the necessary project resource ID. The skill then guides them through a series of phases, including selecting the desired model version and SKU from available options. It also allows users to specify the exact capacity needed for deployment, ensuring that the setup aligns with their project’s demands. Additionally, users can configure content filtering through RAI policies and enable advanced options like dynamic quotas and priority processing, which are crucial for optimizing model performance.
This skill is best suited for scenarios where deployment requirements are specific and detailed. For instance, if a user needs to deploy a model with a certain version and SKU while also setting a particular capacity, this skill provides the necessary tools to do so. The guided approach minimizes the risk of errors and ensures that all configurations are validated before the deployment is executed. However, it is not intended for quick deployments, as users seeking a fast setup with optimal configurations should consider using preset options instead.
When to use it
Use this skill when you need detailed control over the deployment configuration of Azure OpenAI models, including version, SKU, and capacity settings.
When not to use it
This skill is not suitable for users looking for a quick deployment solution, as it focuses on customization rather than speed.
What you can build with it
Precise Model Configuration
When deploying a model that requires specific version and SKU selections, this skill guides you through the necessary steps.
Capacity Management
If your deployment needs a specific capacity setting, this skill allows you to configure that precisely, ensuring optimal performance.
Advanced Deployment Features
For users needing advanced options like dynamic quotas or content filtering, this skill provides the necessary configurations.
How to install Customize Model Deployment
View source1. Install with the skills CLI
npx skills add microsoft/azure-skills/customize --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by microsoftCustomize Model Deployment
Interactive guided workflow for deploying Azure OpenAI models with full customization control over version, SKU, capacity, content filtering, and advanced options.
Quick Reference
| Property | Description |
|---|---|
| Flow | Interactive step-by-step guided deployment |
| Customization | Version, SKU, Capacity, RAI Policy, Advanced Options |
| SKU Support | GlobalStandard, Standard, ProvisionedManaged, DataZoneStandard |
| Best For | Precise control over deployment configuration |
| Authentication | Azure CLI (az login) |
| Tools | Azure CLI, MCP tools (optional) |
When to Use This Skill
Use this skill when you need precise control over deployment configuration:
- ✅ Choose specific model version (not just latest)
- ✅ Select deployment SKU (GlobalStandard vs Standard vs PTU)
- ✅ Set exact capacity within available range
- ✅ Configure content filtering (RAI policy selection)
- ✅ Enable advanced features (dynamic quota, priority processing, spillover)
- ✅ PTU deployments (Provisioned Throughput Units)
Alternative: Use preset for quick deployment to the best available region with automatic configuration.
Comparison: customize vs preset
| Feature | customize | preset |
|---|---|---|
| Focus | Full customization control | Optimal region selection |
| Version Selection | User chooses from available | Uses latest automatically |
| SKU Selection | User chooses (GlobalStandard/Standard/PTU) | GlobalStandard only |
| Capacity | User specifies exact value | Auto-calculated (50% of available) |
| RAI Policy | User selects from options | Default policy only |
| Region | Current region first, falls back to all regions if no capacity | Checks capacity across all regions upfront |
| Use Case | Precise deployment requirements | Quick deployment to best region |
Prerequisites
- Azure subscription with Cognitive Services Contributor or Owner role
- Microsoft Foundry project resource ID (format:
/subscriptions/{sub}/resourceGroups/{rg}/providers/Microsoft.CognitiveServices/accounts/{account}/projects/{project}) - Azure CLI installed and authenticated (
az login) - Optional: Set
PROJECT_RESOURCE_IDenvironment variable
Workflow Overview
Complete Flow (14 Phases)
1. Verify Authentication
2. Get Project Resource ID
3. Verify Project Exists
4. Get Model Name (if not provided)
5. List Model Versions → User Selects
6. List SKUs for Version → User Selects
7. Get Capacity Range → User Configures
7b. If no capacity: Cross-Region Fallback → Query all regions → User selects region/project
8. List RAI Policies → User Selects
9. Configure Advanced Options (if applicable)
10. Configure Version Upgrade Policy
11. Generate Deployment Name
12. Review Configuration
13. Execute Deployment & Monitor
Fast Path (Defaults)
If user accepts all defaults (latest version, GlobalStandard SKU, recommended capacity, default RAI policy, standard upgrade policy), deployment completes in ~5 interactions.
Phase Summaries
⚠️ MUST READ: Before executing any phase, load references/customize-workflow.md for the full scripts and implementation details. The summaries below describe what each phase does — the reference file contains the how (CLI commands, quota patterns, capacity formulas, cross-region fallback logic).
| Phase | Action | Key Details |
|---|---|---|
| 1. Verify Auth | Check az account show; prompt az login if needed | Verify correct subscription is active |
| 2. Get Project ID | Read PROJECT_RESOURCE_ID env var or prompt user | ARM resource ID format required |
| 3. Verify Project | Parse resource ID, call az cognitiveservices account show | Extracts subscription, RG, account, project, region |
| 4. Get Model | List models via az cognitiveservices account list-models | User selects from available or enters custom name |
| 5. Select Version | Query versions for chosen model | Recommend latest; user picks from list |
| 6. Select SKU | Query model catalog + subscription quota, show only deployable SKUs | ⚠️ Never hardcode SKU lists — always query live data |
| 7. Configure Capacity | Query capacity API, validate min/max/step, user enters value | Cross-region fallback if no capacity in current region |
| 8. Select RAI Policy | Present content filter options | Default: Microsoft.DefaultV2 |
| 9. Advanced Options | Dynamic quota (GlobalStandard), priority processing (PTU), spillover | SKU-dependent availability |
| 10. Upgrade Policy | Choose: OnceNewDefaultVersionAvailable / OnceCurrentVersionExpired / NoAutoUpgrade | Default: auto-upgrade on new default |
| 11. Deployment Name | Auto-generate unique name, allow custom override | Validates format: ^[\w.-]{2,64}$ |
| 12. Review | Display full config summary, confirm before proceeding | User approves or cancels |
| 13. Deploy & Monitor | az cognitiveservices account deployment create, poll status | Timeout after 5 min; show endpoint + portal link |
Error Handling
Common Issues and Resolutions
| Error | Cause | Resolution |
|---|---|---|
| Model not found | Invalid model name | List available models with az cognitiveservices account list-models |
| Version not available | Version not supported for SKU | Select different version or SKU |
| Insufficient quota | Capacity > available quota | Skill auto-searches all regions; fails only if no region has quota |
| SKU not supported | SKU not available in region | Cross-region fallback searches other regions automatically |
| Capacity out of range | Invalid capacity value | PREVENTED: Skill validates min/max/step at input (Phase 7) |
| Deployment name exists | Name conflict | Auto-incremented name generation |
| Authentication failed | Not logged in | Run az login |
| Permission denied | Insufficient permissions | Assign Cognitive Services Contributor role |
| Capacity query fails | API/permissions/network error | DEPLOYMENT BLOCKED: Will not proceed without valid quota data |
Troubleshooting Commands
# Check deployment status
az cognitiveservices account deployment show --name <account> --resource-group <rg> --deployment-name <name>
# List all deployments
az cognitiveservices account deployment list --name <account> --resource-group <rg> -o table
# Check quota usage
az cognitiveservices usage list --name <account> --resource-group <rg>
# Delete failed deployment
az cognitiveservices account deployment delete --name <account> --resource-group <rg> --deployment-name <name>
Selection Guides & Advanced Topics
For SKU comparison tables, PTU sizing formulas, and advanced option details, load references/customize-guides.md.
SKU selection: GlobalStandard (production/HA) → Standard (dev/test) → ProvisionedManaged (high-volume/guaranteed throughput) → DataZoneStandard (data residency).
Capacity: TPM-based SKUs range from 1K (dev) to 100K+ (large production). PTU-based use formula: (Input TPM × 0.001) + (Output TPM × 0.002) + (Requests/min × 0.1).
Advanced options: Dynamic quota (GlobalStandard only), priority processing (PTU only, extra cost), spillover (overflow to backup deployment).
Related Skills
- preset - Quick deployment to best region with automatic configuration
- microsoft-foundry - Parent skill for all Microsoft Foundry operations
- quota — For quota viewing, increase requests, and troubleshooting quota errors, defer to this skill instead of duplicating guidance
- rbac - Manage permissions and access control
Notes
- Set
PROJECT_RESOURCE_IDenvironment variable to skip prompt - Not all SKUs available in all regions; capacity varies by subscription/region/model
- Custom RAI policies can be configured in Azure Portal
- Automatic version upgrades occur during maintenance windows
- Use Azure Monitor and Application Insights for production deployments
Frequently asked questions about Customize Model Deployment
Similar skills
WinMD API Search
Easily find and explore Windows desktop APIs.
WebMCPify
Transform any web app into an agent-ready platform.
Phoenix Tracing
Instrument LLM applications with OpenInference tracing.
Foundry Hosted Agent CopilotKit
Guidance for developing agentic web apps on Azure.
Power Automate Foundation
Connect AI agents to Power Automate seamlessly.
Power Automate Flow Builder
Efficiently build and deploy Power Automate flows programmatically.
