1. Kubernetes Ingress for AI API Gateway


    To set up a Kubernetes Ingress for an AI API Gateway, you would typically use the Kubernetes resource Ingress which is an API object that manages external access to the services in a cluster, typically HTTP. Ingress may provide load balancing, SSL termination, and name-based virtual hosting.

    Below is a Pulumi program written in Python which creates a Kubernetes Ingress for an AI API Gateway. This Ingress will route traffic based on the specified rules to the backend service, which can be our AI API Gateway running within our Kubernetes cluster.

    Before we jump into the actual code, here's a step-by-step breakdown of what the Pulumi program will do:

    1. Import necessary Pulumi packages for Kubernetes.
    2. Define a Kubernetes service for AI API Gateway which will be exposed via Ingress.
    3. Create a Kubernetes Ingress resource to manage the external access to the AI API Gateway service.
      • Define the Ingress spec with rules that determine how requests are forwarded to the AI API Gateway service.
      • Optionally specify TLS for secure connection. For simplicity, this part is omitted in our example, but it should be considered for production environments.

    Let's take a look at the program:

    import pulumi import pulumi_kubernetes as k8s # Define the API Gateway service that we want to expose via Ingress api_service = k8s.core.v1.Service('api-gateway-service', spec={ 'selector': {'app': 'ai-api-gateway'}, # Must match labels of the AI API Gateway pods 'ports': [{ 'port': 80, # Port that the service will serve on 'targetPort': 8080 # Port on the container that the service should direct traffic to }] } ) # Define the Ingress resource to route external requests to the API Gateway service api_ingress = k8s.networking.v1.Ingress('api-gateway-ingress', metadata={ 'name': 'api-gateway', # Name of the Ingress resource 'annotations': { # annotations can be used to customize behavior with some ingress controllers 'kubernetes.io/ingress.class': 'nginx' # assuming you have an NGINX ingress controller } }, spec={ 'rules': [{ 'http': { 'paths': [{ 'path': '/ai-api', # Path for the AI API Gateway 'pathType': 'Prefix', # Match on this prefix 'backend': { 'service': { 'name': api_service.metadata['name'], 'port': { 'number': 80 # Service port defined above } } } }] } }] } ) # Export the Ingress resource name pulumi.export('ingress_name', api_ingress.metadata['name'])

    In the program above, we first import the pulumi and pulumi_kubernetes packages.

    We then create a Kubernetes Service api-gateway-service for the AI API Gateway deployment. We assume that the AI API Gateway is deployed with a label app: ai-api-gateway which will be used by the Service selector to route internal traffic to the pods with matching labels. The service is set to serve on port 80, targeting containers' port 8080.

    The next step is to create an Ingress resource api-gateway-ingress. This resource has an annotation specifying that it will be managed by an NGINX ingress controller — this assumes your Kubernetes cluster is already configured with an NGINX ingress controller.

    The Ingress resource has a rule that defines how external traffic should route to the services - in this case, all traffic on the path /ai-api should route to the api-gateway-service. The pathType is set to Prefix, which means any path starting with /ai-api will be matched.

    Lastly, we export the Ingress resource name so it can be easily retrieved with the Pulumi CLI.

    Keep in mind that you will need a running Kubernetes cluster with an NGINX Ingress controller or any other Ingress controller that you wish to use. The specifics of that setup would depend on your environment and are outside the scope of this Kubernete Ingress example.