1. Secure AI API Gateway using Kong on Kubernetes


    To deploy a secure AI API Gateway using Kong on Kubernetes with Pulumi, we first need to establish a Kubernetes cluster where Kong will be deployed. For the purpose of this guide, we will assume that a Kubernetes cluster is already running and is accessible via kubectl.

    In general, securing an API using Kong involves setting up Kong as an ingress controller for your Kubernetes cluster. You may also want to configure authentication using plugins like JWT, OAuth 2.0, or your custom authentication mechanism. We will set up a Kong Consumer, secure the API using a JWT Plugin, and create Routes and Services for handling the API requests.

    The following Pulumi Python program will help you to set up Kong on Kubernetes:

    1. Kong Consumer: Represents a consumer of your API. In a real-world scenario, this would be an end-user or external service that interacts with your API.
    2. Kong JWT Plugin: Attaches JWT authentication to a consumer, so only requests with a valid JWT token are allowed.
    3. Kong Service: Defines the external service (your AI API) that Kong will gateway to.
    4. Kong Route: Defines rules to match client requests to services, like path, host, or headers.

    Let's create a Pulumi program that sets up these resources:

    import pulumi import pulumi_kong as kong # Create a Kong Consumer that represents a user or service using the API. consumer = kong.Consumer("consumer", username="ai-service-consumer") # Set up a plugin for JWT authentication for the consumer we just created. # In a real-world scenario, you'd want to securely store the key and secret. jwt_auth = kong.Plugin("jwt-auth", name="jwt", consumer_id=consumer.id, config_json=pulumi.Output.secret("{\"key\": \"your-key\", \"secret\": \"your-secret\"}")) # Assume there is a pre-existing Kubernetes Service for the AI API. # Replace `ai-api-service-name` and `ai-api-service-port` with your real service name and port. api_service = kong.Service("api-service", name="ai-api-service", host="ai-api-service-name", # Kubernetes Service name of the AI API port=ai-api-service-port, # Port on which the AI API service is exposed protocol="http") # Define a Route for the AI API service which only allows requests with valid JWT tokens. api_route = kong.Route("api-route", service_id=api_service.id, paths=["/ai-api"], protocols=["http", "https"], strip_path=True) # Export the information that might be useful. pulumi.export('consumer_id', consumer.id) pulumi.export('jwt_auth_plugin_id', jwt_auth.id) pulumi.export('api_service_id', api_service.id) pulumi.export('api_route_id', api_route.id)

    Here is what each section of the code is doing:

    • We import the required modules, pulumi and pulumi_kong.
    • We define a Consumer to represent the user or service that will be consuming our AI API.
    • We configure the JWT Plugin to secure the API. The config_json should include the key and secret used for JWT token validation. It's important to handle these credentials securely.
    • We create a Service to tell Kong how to route to our AI API.
    • We create a Route that defines how client requests are matched to Services. The route is configured to handle requests to the "/ai-api" path.
    • Finally, we export some of the IDs of the resources we created for reference or for use in other programs or outputs.

    Important: This is a simplified representation meant for educational purposes, and as such, error handling and security best practices like storing secrets securely have been omitted for clarity. When implementing your solution, pay close attention to these aspects.

    If you haven't already set up Pulumi or Kubernetes, you'll need to do so. For Pulumi, install the CLI and set up an account. For Kubernetes, ensure you have a cluster available and kubectl is configured to communicate with the cluster. The Pulumi CLI will integrate with your kubectl configuration to deploy to your cluster.