azure-aigateway

Configure Azure API Management as an AI gateway with governance policies.

66|41|Updated Mar 4, 2026

Search Tags:#azure #policy #content-safety #ai-gateway #token-metrics #apim #semantic-caching

Ready to run

npx skills add https://github.com/olivomarco/vbd-copilot --skill azure-aigateway-olivomarco

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: azure-aigateway
Source: https://github.com/olivomarco/vbd-copilot/tree/main/skills/azure-aigateway
Command: npx skills add https://github.com/olivomarco/vbd-copilot --skill azure-aigateway-olivomarco

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Azure API Management can be configured as a centralized AI gateway to govern models, tools, and agents, enabling secure, scalable AI workflows.

Core Features & Use Cases

Governance for AI models, MCP tools, and agents within APIM.
Apply safety, rate limits, semantic caching, and token metrics to AI deployments.
Use cases include building scalable AI gateways for deployments, testing, and governance.

Quick Start

Provision a sample APIM gateway and run a test call to verify governance policies.

Frequently Asked Questions about azure-aigateway

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I configure Azure API Management as an AI gateway?▼

To configure Azure API Management as an AI gateway, you provision APIM, wire multiple backends, and apply inbound governance policies like semantic caching, token limits, and content safety across your AI models and agents.

What governance policies can I apply to AI models in APIM?▼

You can apply governance policies for AI models in APIM including semantic caching, token limits, content safety filtering, load balancing, and observability metrics to ensure secure and scalable AI workflows.

Do I need a specific APIM deployment pattern to use AI gateway policies?▼

Yes, using AI gateway policies requires prerequisites like deploying APIM with the azure-prepare pattern, wiring backends, and applying the specific inbound policy configurations described for your deployment scenario.

Can Azure API Management handle load balancing and token metrics for multiple AI backends?▼

Yes, Azure API Management handles load balancing and token metrics across multiple AI backends, allowing you to centralize governance and observability for various models, MCP tools, and agents within a single gateway.

How does semantic caching work in an Azure AI gateway?▼

Semantic caching in an Azure AI gateway stores and retrieves semantically similar prompt responses through APIM policies, reducing backend load and improving response times for repeated AI model queries.

What is the best way to test AI gateway governance policies in Azure APIM?▼

The best way to test AI gateway governance policies is to provision a sample APIM gateway and run test calls to verify that content safety, token limits, and semantic caching rules apply correctly to your requests.