azure-aigateway

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.

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.

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.

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.

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.

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.