Amazon Bedrock

Configure Amazon Bedrock as an LLM provider in agentgateway.

Before you begin link

1. Set up an agentgateway proxy.
2. Make sure that your Amazon credentials have access to the Bedrock models that you want to use. You can alternatively use an AWS Bedrock API key.

Set up access to Amazon Bedrock link

1. Store your credentials to access the AWS Bedrock API.

   1. Log in to the AWS console and store your access credentials as environment variables.

        export AWS_ACCESS_KEY_ID="<aws-access-key-id>"
      export AWS_SECRET_ACCESS_KEY="<aws-secret-access-key>"
      export AWS_SESSION_TOKEN="<aws-session-token>"
        

   2. Create a secret with your Bedrock API key. Optionally provide the session token.

        kubectl create secret generic bedrock-secret \
        -n gloo-system \
        --from-literal=accessKey="$AWS_ACCESS_KEY_ID" \
        --from-literal=secretKey="$AWS_SECRET_ACCESS_KEY" \
        --from-literal=sessionToken="$AWS_SESSION_TOKEN" \
        --type=Opaque \
        --dry-run=client -o yaml | kubectl apply -f -
        

   1. Save the API key in an environment variable.

        export BEDROCK_API_KEY=<insert your API key>
        

   2. Create a Kubernetes secret to store your Amazon Bedrock API key.

        kubectl apply -f- <<EOF
      apiVersion: v1
      kind: Secret
      metadata:
        name: bedrock-secret
        namespace: gloo-system
      type: Opaque
      stringData:
        Authorization: $BEDROCK_API_KEY
      EOF
        

   2. Create a Backend resource to configure an LLM provider that references the AI API key secret.

     kubectl apply -f- <<EOF
   apiVersion: gateway.kgateway.dev/v1alpha1
   kind: Backend
   metadata:
     name: bedrock
     namespace: gloo-system
   spec:
     type: AI
     ai:
       llm:
         bedrock:
           model: "amazon.nova-micro-v1:0"
           region: us-east-1
           auth:
             type: Secret
             secretRef:
               name: bedrock-secret
   EOF
     

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

   Setting	Description
   type	Set to AI to configure this Backend for an AI provider.
   ai	Define the AI backend configuration. The example uses Amazon Bedrock (spec.ai.llm.bedrock).
   model	The model to use to generate responses. In this example, you use the amazon.nova-micro-v1:0 model. Keep in mind that some models support cross-region inference. These models begin with a us. prefix, such as us.anthropic.claude-sonnet-4-20250514-v1:0. For more models, see the AWS Bedrock docs.
   region	The AWS region where your Bedrock model is deployed. Multiple regions are not supported.
   auth	Provide the credentials to use to access the Amazon Bedrock API. The example refers to the secret that you previously created. To use IRSA, omit the auth settings.

2. Create an HTTPRoute resource to route requests through your agentgateway proxy to the Bedrock Backend. Note that Gloo Gateway automatically rewrites the endpoint that you set up (such as /bedrock) to the appropriate chat completion endpoint of the LLM provider for you, based on the LLM provider that you set up in the Backend resource.

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

3. Send a request to the LLM provider API. Verify that the request succeeds and that you get back a response from the chat completion API.

     curl "$INGRESS_GW_ADDRESS/bedrock" -H content-type:application/json -d '{
       "model": "",
       "messages": [
         {
           "role": "user",
           "content": "You are a cloud native solutions architect, skilled in explaining complex technical concepts such as API Gateway, microservices, LLM operations, kubernetes, and advanced networking patterns. Write me a 20-word pitch on why I should use an AI gateway in my Kubernetes cluster."
         }
       ]
     }' | jq
     

     curl "localhost:8080/bedrock" -H content-type:application/json  -d '{
       "model": "",
       "messages": [
         {
           "role": "user",
           "content": "You are a cloud native solutions architect, skilled in explaining complex technical concepts such as API Gateway, microservices, LLM operations, kubernetes, and advanced networking patterns. Write me a 20-word pitch on why I should use an AI gateway in my Kubernetes cluster."
         }
       ]
     }' | jq
     

   Example output:

     {
     "metrics": {
       "latencyMs": 2097
     },
     "output": {
       "message": {
         "content": [
           {
             "text": "\nAn AI gateway in your Kubernetes cluster can enhance performance, scalability, and security while simplifying complex operations. It provides a centralized entry point for AI workloads, automates deployment and management, and ensures high availability."
           }
         ],
         "role": "assistant"
       }
     },
     "stopReason": "end_turn",
     "usage": {
       "inputTokens": 60,
       "outputTokens": 47,
       "totalTokens": 107
     }
   }