1. Secure, Multi-Tenant AI Services with Ambassador Edge Stack


    If you are looking to build secure, multi-tenant AI services using Ambassador Edge Stack, we will need to use Kubernetes as the underlying platform for container orchestration. Ambassador Edge Stack is a popular Kubernetes-native API Gateway that manages ingress traffic to your services. It supports multi-tenancy, which allows you to isolate different tenants' traffic and configurations logically.

    We'll create a foundational Pulumi program using Python that deploys Ambassador Edge Stack to a Kubernetes cluster, ensuring you have a solid base to build upon. This example assumes you already have a Kubernetes cluster provisioned.

    Detailed Explanation:

    1. Kubernetes Cluster: You'll need an existing Kubernetes cluster. This could be any cloud Kubernetes service like Amazon EKS, Google GKE, or Microsoft AKS, or a self-managed cluster.

    2. Ambassador Edge Stack: We'll install Ambassador Edge Stack, which will act as our API Gateway managing the ingress traffic and applying security rules per tenant.

    3. AI Services Deployment: While not fully covered in the program below, the next step after setting up Ambassador would be to deploy your multi-tenant AI services into Kubernetes. Each AI service would run in its own set of pods, isolated by namespaces or other Kubernetes primitives to achieve multi-tenancy.

    4. Security: We will use Ambassador's built-in features to secure our services. Ambassador integrates with different authentication services, and you can configure it based on your precise security requirements.

    5. Pulumi Kubernetes Provider: To deploy these services to Kubernetes, we use the Pulumi Kubernetes Provider. This provider allows you to declare Kubernetes resources in your Pulumi program.

    The following Pulumi program lays the groundwork for deploying Ambassador Edge Stack to your Kubernetes cluster:

    import pulumi from pulumi_kubernetes.helm.v3 import Chart, ChartOpts # The name of the Kubernetes namespace in which we'll deploy Ambassador namespace_name = "ambassador" # Deploy Ambassador Edge Stack using the Kubernetes Helm Chart ambassador_chart = Chart( "ambassador", config=ChartOpts( chart="ambassador", version="6.5.13", # Use the version you require namespace=namespace_name, fetch_opts={"repo": "https://www.getambassador.io"}, values={ "service": {"type": "LoadBalancer"}, "adminService": {"type": "NodePort"}, # You can specify additional configuration here to customize Ambassador } ) ) # Export the public IP or Hostname of the Ambassador Load Balancer load_balancer = pulumi.Output.from_input(ambassador_chart.resources["v1/Service", "ambassador/ambassador"]).apply( lambda service: service.status.load_balancer.ingress[0].hostname or service.status.load_balancer.ingress[0].ip ) pulumi.export("ambassador_hostname", load_balancer)

    This is a base program; more customizations are likely needed, such as setting up domain names, configuring security settings, creating per-tenant routing rules, etc. Ambassador Edge Stack is highly configurable, and you can extend this program to fit the exact needs of your multi-tenant AI services setup.

    Before deploying this Pulumi program, you'll need to install Pulumi and configure Kubernetes access. Once those prerequisites are met, you can run the program using the Pulumi CLI to deploy Ambassador to your cluster.

    This script shows you how to deploy the Ambassador API Gateway, which provides the necessary infrastructure to expose your multi-tenant AI services securely. From here, you would need to deploy the actual services and configure Ambassador to route and secure traffic appropriately.