descriptionConfigure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, configure AI backend, token metrics, AI cost control, convert API to MCP, import OpenAPI to gateway. Azure AI Gateway
Configure Azure API Management (APIM) as an AI Gateway for governing AI models, MCP tools, and agents.
To deploy APIM, use the azure-prepare skill. See APIM deployment guide.
When to Use This Skill
Quick Reference
Get Gateway Details
# Get gateway URL
az apim show --name <apim-name> --resource-group <rg> --query "gatewayUrl" -o tsv
# List backends (AI models)
az apim backend list --service-name <apim-name> --resource-group <rg> \
--query "[].{id:name, url:url}" -o table
# Get subscription key
az apim subscription keys list \
--service-name <apim-name> --resource-group <rg> --subscription-id <sub-id>
Test AI Endpoint
GATEWAY_URL=$(az apim show --name <apim-name> --resource-group <rg> --query "gatewayUrl" -o tsv)
curl -X POST "${GATEWAY_URL}/openai/deployments/<deployment>/chat/completions?api-version=2024-02-01" \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: <key>" \
-d '{"messages": [{"role": "user", "content": "Hello"}], "max_tokens": 100}'
Common Tasks
Add AI Backend
See references/patterns.md for full steps.
# Discover AI resources
az cognitiveservices account list --query "[?kind=='OpenAI']" -o table
# Create backend
az apim backend create --service-name <apim> --resource-group <rg> \
--backend-id openai-backend --protocol http --url "https://<aoai>.openai.azure.com/openai"
# Grant access (managed identity)
az role assignment create --assignee <apim-principal-id> \
--role "Cognitive Services User" --scope <aoai-resource-id>
Apply AI Governance Policy
Recommended policy order in <inbound>:
- Authentication - Managed identity to backend
- Semantic Cache Lookup - Check cache before calling AI
- Token Limits - Cost control
- Content Safety - Filter harmful content
- Backend Selection - Load balancing
- Metrics - Token usage tracking
See references/policies.md for complete example.
Troubleshooting
See references/troubleshooting.md for details.
References
SDK Quick References
Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailb