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azure-aigateway

Configures Azure API Management as an AI Gateway with policies for security, caching, and load balancing.

242|189|Updated Oct 28, 2024
Search Tags:#rate limiting#openapi#azure#semantic caching#content safety#ai gateway#apim
One-click install
npx skills add https://github.com/microsoft/GitHub-Copilot-for-Azure --skill azure-aigateway
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: azure-aigateway
Source: https://github.com/microsoft/GitHub-Copilot-for-Azure/tree/main/plugin/skills/azure-aigateway
Command: npx skills add https://github.com/microsoft/GitHub-Copilot-for-Azure --skill azure-aigateway

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Configure Azure API Management (APIM) as an AI Gateway to secure, observe, and control AI models, MCP servers, and agents.

Core Features & Use Cases

  • Security and access control for AI backends via APIM policies and managed identities.
  • Observability and governance of AI workloads with rate limiting, token quotas, and logging.
  • Load balancing, semantic caching, and content safety for AI endpoints.
  • Simple OpenAPI-to-MCP workflows to expose APIs as AI-enabled tools.

Quick Start

Bootstrap an AI gateway by deploying APIM Basicv2, enabling managed identity, wiring a backend to your AI service, and applying policies for caching, rate limiting, and content safety. Then test the gateway URL and iterate with your MCP setup or OpenAPI imports.

Frequently Asked Questions about azure-aigateway

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

FAQPage Schema
How do I set up an AI gateway using Azure API Management?▼

To set up an AI gateway using Azure API Management, deploy APIM Basicv2, enable managed identity, wire your backend to the AI service, and apply policies for semantic caching, rate limiting, and content safety. You then test the gateway URL and iterate with MCP or OpenAPI imports.

What is semantic caching and how does it work for AI endpoints?▼

Semantic caching for AI endpoints stores responses to similar queries, reducing redundant backend calls. Configured via Azure API Management policies, it improves performance by serving cached AI responses for semantically identical requests, controlling AI workloads and lowering latency.

Can I expose OpenAPI workflows as tools for AI agents?▼

Yes, you can expose OpenAPI workflows as AI-enabled tools. The Azure API Management AI gateway supports simple OpenAPI-to-MCP workflows, allowing you to import OpenAPI specifications and expose them as MCP servers for consumption by AI agents.

Does Azure API Management support content safety and rate limiting for AI models?▼

Azure API Management supports content safety and rate limiting for AI models through configurable gateway policies. It provides observability and governance by enforcing token quotas, applying content safety filters, and logging AI workload activities across endpoints.

How do I load balance across multiple AI backends in APIM?▼

To load balance across multiple AI backends in APIM, configure backend integration policies within your AI gateway. This distributes incoming traffic across various AI service endpoints, ensuring high availability and reliable control over your AI models and APIs.

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