Azure

Configuration and setup for Azure AI services provider

Configure Azure as an LLM provider in agentgateway.

Azure supports two endpoint types:

• Azure OpenAI (openAI): Connect to Azure OpenAI Service deployments at {resourceName}.openai.azure.com.
• Azure AI Foundry (foundry): Connect to Azure AI Foundry project endpoints at {resourceName}-resource.services.ai.azure.com.

Before you begin

Install and set up an agentgateway proxy.

Set up access to Azure

1. Retrieve the resource name and, if applicable, the project name from the Azure AI Foundry portal or the Azure portal. For example:

   • For an Azure OpenAI endpoint like https://{my-resource}.openai.azure.com, the resource name is my-resource.
   • For an Azure AI Foundry endpoint like https://{my-resource}-resource.services.ai.azure.com and path /api/projects/{my-project}, the resource name is my-resource and the project name is my-project. If the resource name and the project name are the same, you can leave the projectName field empty.

2. Store the API key to access your model deployment in an environment variable. If you are using implicit Entra ID authentication (such as managed identity or workload identity), you can skip this step.

   export AZURE_API_KEY=<insert your model deployment key>

3. Create a Kubernetes secret to store your API key. If you are using implicit Entra ID authentication, skip this step.

   kubectl apply -f- <<EOF
   apiVersion: v1
   kind: Secret
   metadata:
     name: azure-secret
     namespace: agentgateway-system
   type: Opaque
   stringData:
     Authorization: $AZURE_API_KEY
   EOF

4. Create an AgentgatewayBackend resource to configure the Azure LLM provider.

   Azure OpenAI (API key)Azure AI Foundry (API key)Azure OpenAI (implicit auth)

   kubectl apply -f- <<EOF
   apiVersion: agentgateway.dev/v1alpha1
   kind: AgentgatewayBackend
   metadata:
     name: azure
     namespace: agentgateway-system
   spec:
     ai:
       provider:
         azure:
           resourceName: my-resource
           resourceType: openAI
           model: gpt-4.1-mini
   EOF

   kubectl apply -f- <<EOF
   apiVersion: agentgateway.dev/v1alpha1
   kind: AgentgatewayBackend
   metadata:
     name: azure
     namespace: agentgateway-system
   spec:
     ai:
       provider:
         azure:
           resourceName: my-resource
           resourceType: foundry
           projectName: my-project
           model: gpt-4.1-mini
   EOF

   When you use implicit Entra ID authentication, the gateway automatically obtains a token using DefaultAzureCredential. No secret or policies.auth is required. This works with managed identity, workload identity, or Azure CLI credentials.

   kubectl apply -f- <<EOF
   apiVersion: agentgateway.dev/v1alpha1
   kind: AgentgatewayBackend
   metadata:
     name: azure
     namespace: agentgateway-system
   spec:
     ai:
       provider:
         azure:
           resourceName: my-resource
           resourceType: openAI
           model: gpt-4.1-mini
   EOF

   Review the following table to understand this configuration. For more information, see the API reference.

   Setting	Description
   ai.provider.azure	Define the Azure provider.
   azure.resourceName	The Azure resource name used to construct the endpoint hostname.
   azure.resourceType	The endpoint type: openAI for Azure OpenAI Service, or foundry for Azure AI Foundry.
   azure.model	The model to use for requests, such as gpt-4.1-mini.
   azure.projectName	The Foundry project name. Required when resourceType is foundry.
   azure.apiVersion	Optional API version override. Defaults to v1. For legacy deployments, use a dated version like 2025-01-01-preview.

5. Create an HTTPRoute resource that routes incoming traffic to the AgentgatewayBackend. The following example sets up a route. Note that agentgateway automatically rewrites the endpoint to the appropriate chat completion endpoint of the LLM provider for you, based on the LLM provider that you set up in the AgentgatewayBackend resource.

   OpenAI-compatible v1/chat/completions Custom route

   kubectl apply -f- <<EOF
   apiVersion: gateway.networking.k8s.io/v1
   kind: HTTPRoute
   metadata:
     name: azure
     namespace: agentgateway-system
   spec:
     parentRefs:
       - name: agentgateway-proxy
         namespace: agentgateway-system
     rules:
     - backendRefs:
       - name: azure
         namespace: agentgateway-system
         group: agentgateway.dev
         kind: AgentgatewayBackend
   EOF

   kubectl apply -f- <<EOF
   apiVersion: gateway.networking.k8s.io/v1
   kind: HTTPRoute
   metadata:
     name: azure
     namespace: agentgateway-system
   spec:
     parentRefs:
       - name: agentgateway-proxy
         namespace: agentgateway-system
     rules:
     - matches:
       - path:
           type: PathPrefix
           value: /azure
       backendRefs:
       - name: azure
         namespace: agentgateway-system
         group: agentgateway.dev
         kind: AgentgatewayBackend
   EOF

6. Send a request to the LLM provider API along the route that you previously created. Verify that the request succeeds and that you get back a response from the chat completion API.

   OpenAI-compatible v1/chat/completions Custom route

   Cloud Provider LoadBalancer:

   curl "$INGRESS_GW_ADDRESS/v1/chat/completions" -H content-type:application/json  -d '{
      "model": "",
      "messages": [
        {
          "role": "system",
          "content": "You are a helpful assistant."
        },
        {
          "role": "user",
          "content": "Write a short haiku about cloud computing."
        }
      ]
    }' | jq

   Localhost:

   curl "localhost:8080/v1/chat/completions" -H content-type:application/json  -d '{
      "model": "",
      "messages": [
        {
          "role": "system",
          "content": "You are a helpful assistant."
        },
        {
          "role": "user",
          "content": "Write a short haiku about cloud computing."
        }
      ]
    }' | jq

   Cloud Provider LoadBalancer:

   curl "$INGRESS_GW_ADDRESS/azure" -H content-type:application/json  -d '{
      "model": "",
      "messages": [
        {
          "role": "system",
          "content": "You are a helpful assistant."
        },
        {
          "role": "user",
          "content": "Write a short haiku about cloud computing."
        }
      ]
    }' | jq

   Localhost:

   curl "localhost:8080/azure" -H content-type:application/json  -d '{
      "model": "",
      "messages": [
        {
          "role": "system",
          "content": "You are a helpful assistant."
        },
        {
          "role": "user",
          "content": "Write a short haiku about cloud computing."
        }
      ]
    }' | jq

   Example output:

   {
     "id": "chatcmpl-9A8B7C6D5E4F3G2H1",
     "object": "chat.completion",
     "created": 1727967462,
     "model": "gpt-4.1-mini",
     "choices": [
       {
         "index": 0,
         "message": {
           "role": "assistant",
           "content": "Floating servers bright,\nData streams through endless sky,\nClouds hold all we need."
         },
         "finish_reason": "stop"
       }
     ],
     "usage": {
       "prompt_tokens": 28,
       "completion_tokens": 19,
       "total_tokens": 47
     }
   }

Next steps