Cloud LLM providers

Set up cloud LLM providers with AI Gateway.

Before you begin

1. Set up AI Gateway.
2. Choose a supported LLM provider.

Supported LLM providers

The examples throughout the AI Gateway docs use OpenAI as the LLM provider, but you can use other providers that are supported by AI Gateway.

Cloud providers

Kgateway supports the following AI cloud providers:

• Anthropic
• Azure OpenAI
• Gemini
• OpenAI. You can also use openai support for LLM providers that use the OpenAI API, such as DeepSeek and Mistral.
• Vertex AI

Local providers

You can use kgateway with a local LLM provider, such as the following common options:

• Ollama for local LLM development.
• Gateway API Inference Extension project to route requests to local LLM workloads that run in your cluster.

OpenAI

OpenAI is the most common LLM provider, and the examples throughout the AI Gateway docs use OpenAI. You can adapt these examples to your own provider, especially ones that use the OpenAI API, such as DeepSeek and Mistral.

To set up OpenAI, continue with the Authenticate to the LLM guide.

Gemini

1. Save your Gemini API key as an environment variable. To retrieve your API key, log in to the Google AI Studio and select API Keys.

   export GOOGLE_KEY=<your-api-key>

2. Create a secret to authenticate to Google. For other ways to authenticate, see the Auth guide.

   kubectl apply -f - <<EOF
   apiVersion: v1
   kind: Secret
   metadata:
     name: google-secret
     namespace: kgateway-system
     labels:
       app: ai-kgateway
   type: Opaque
   stringData:
     Authorization: $GOOGLE_KEY 
   EOF

3. Create a Backend resource to define the Gemini destination.

   kubectl apply -f- <<EOF
   apiVersion: gateway.kgateway.dev/v1alpha1
   kind: Backend
   metadata:
     labels:
       app: ai-kgateway
     name: google
     namespace: kgateway-system
   spec:
     ai:
       llm:
         provider:
           gemini:
             apiVersion: v1beta
             authToken:
               kind: SecretRef
               secretRef:
                 name: google-secret
             model: gemini-1.5-flash-latest
     type: AI
   EOF
   Review the following table to understand this configuration.

   Setting	Description
   gemini	The Gemini AI provider.
   apiVersion	The API version of Gemini that is compatible with the model that you plan to use. In this example, you must use v1beta because the gemini-1.5-flash-latest model is not compatible with the v1 API version. For more information, see the Google AI docs.
   authToken	The authentication token to use to authenticate to the LLM provider. The example refers to the secret that you created in the previous step.
   model	The model to use to generate responses. In this example, you use the gemini-1.5-flash-latest model. For more models, see the Google AI docs.

4. Create an HTTPRoute resource to route requests to the Gemini backend. Note that kgateway automatically rewrites the endpoint that you set up (such as /gemini) 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: google
     namespace: kgateway-system                           
     labels:
       app: ai-kgateway
   spec:
     parentRefs:
       - name: ai-gateway
         namespace: kgateway-system
     rules:
     - matches:
       - path:
           type: PathPrefix
           value: /gemini
       backendRefs:
       - name: google
         namespace: kgateway-system
         group: gateway.kgateway.dev
         kind: Backend
   EOF

5. 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.

   Cloud Provider LoadBalancerPort-forward for local testing

   curl "$INGRESS_GW_ADDRESS:8080/gemini" -H content-type:application/json  -d '{
     "contents": [                         
       {          
         "parts": [
           {     
             "text": "Explain how AI works in a few words"
           }
         ]             
       }
     ]          
   }' | jq  

   curl "localhost:8080/gemini" -H content-type:application/json -d '{
     "contents": [                         
       {          
         "parts": [
           {     
             "text": "Explain how AI works in a few words"
           }
         ]             
       }
     ]          
   }' | jq 

   Example output:

   {
     "candidates": [
       {
         "content": {
           "parts": [
             {
               "text": "Learning patterns from data to make predictions.\n"
             }
           ],
           "role": "model"
         },
         "finishReason": "STOP",
         "avgLogprobs": -0.017732446392377216
       }
     ],
     "usageMetadata": {
       "promptTokenCount": 8,
       "candidatesTokenCount": 9,
       "totalTokenCount": 17,
       "promptTokensDetails": [
         {
           "modality": "TEXT",
           "tokenCount": 8
         }
       ],
       "candidatesTokensDetails": [
         {
           "modality": "TEXT",
           "tokenCount": 9
         }
       ]
     },
     "modelVersion": "gemini-1.5-flash-latest",
     "responseId": "UxQ6aM_sKbjFnvgPocrJaA"
   }

Next

Now that you can send requests to an LLM provider, explore the other AI Gateway features.