Scalable Multi-Tenant AI Services with Kong on Kubernetes

Question

Pulumi · Accepted Answer

To deploy scalable multi-tenant AI services with Kong on Kubernetes using Pulumi, you'd typically follow these steps:

1. Set up a Kubernetes cluster where you'll deploy your services.
2. Deploy Kong as an Ingress controller to manage and route external traffic.
3. Define your AI services as Kubernetes deployments.
4. Use Kong to manage traffic, enforce policies, and ensure security between tenants.

Below is a Pulumi program that demonstrates how to set up such an environment. We'll use the `pulumi_kubernetes` package to interact with Kubernetes resources and the `kong` provider to configure Kong-specific resources such as Service, Consumer, Route, Plugin, etc.

First, ensure you have [Pulumi installed](https://www.pulumi.com/docs/get-started/install/) and [configured for Kubernetes](https://www.pulumi.com/docs/intro/cloud-providers/kubernetes/setup/).

Now, let's look at the Pulumi program:

```python
import pulumi
import pulumi_kubernetes as k8s
import pulumi_kong as kong

# Create a Kubernetes Namespace for Kong, isolating it from other services.
kong_namespace = k8s.core.v1.Namespace("kong-namespace", metadata={"name": "kong"})

# Deploy Kong as an Ingress Controller within the kong namespace.
# This assumes you have a pre-defined configuration file. You may need to customize the details for your specific environment.
kong_release = k8s.helm.v3.Release("kong",
    args=k8s.helm.v3.ReleaseArgs(
        chart="kong",
        version="2.5.0",  # Change to the desired version number.
        namespace=kong_namespace.metadata["name"],
        name="kong",
        values={
            "ingressController": {
                "installCRDs": False,  # Assume CRDs are installed separately
            },
        },
    )
)

# Define your AI service deployment in Kubernetes. This example will deploy a dummy service, replace it with your actual AI service.
ai_service_app = k8s.apps.v1.Deployment("ai-service-app",
    metadata={
        "namespace": kong_namespace.metadata["name"],
    },
    spec={
        "selector": {
            "matchLabels": {
                "app": "ai-service",
            },
        },
        "replicas": 2,
        "template": {
            "metadata": {
                "labels": {
                    "app": "ai-service",
                },
            },
            "spec": {
                "containers": [{
                    "name": "ai-service",
                    "image": "my-ai-service:latest",  # Replace with your actual Docker image.
                }],
            },
        },
    },
    opts=pulumi.ResourceOptions(depends_on=[kong_release])
)

# Expose your AI service using a Kubernetes Service within the kong namespace.
ai_service = k8s.core.v1.Service("ai-service",
    metadata={
        "namespace": kong_namespace.metadata["name"],
        "labels": {
            "app": "ai-service",
        },
    },
    spec={
        "selector": {
            "app": "ai-service",
        },
        "ports": [{
            "protocol": "TCP",
            "port": 80,
            "targetPort": 8080,  # Update the target port based on your app configuration.
        }],
        "type": "ClusterIP",  # Use ClusterIP for internal services, NodePort or LoadBalancer for external services if needed.
    },
    opts=pulumi.ResourceOptions(depends_on=[ai_service_app])
)

# Configure Kong to route and manage traffic to your AI service.
kong_service_route = kong.Route("ai-service-route",
    hosts=["ai-service.example.com"],  # Change to your service's domain.
    protocols=["http", "https"],
    service=kong.ServiceArgs(
        protocol="http",
        host=ai_service.metadata["name"],
        port=80,
        path="/",
        name="ai-service",
        namespace=kong_namespace.metadata["name"]
    ),
    opts=pulumi.ResourceOptions(depends_on=[ai_service])
)

# Export the DNS name of the Kong ingress to access your AI service.
pulumi.export('ai_service_url', pulumi.Output.concat('http://ai-service.example.com'))
```

In this program:

- A `Namespace` is created to isolate the Kong services within Kubernetes.
- Kong is installed using a `Helm` chart. You would need to define the specific version and potential custom values for your use case.
- An example AI service deployment is defined. Replace `my-ai-service:latest` with the actual image you would be deploying.
- A Kubernetes `Service` is created to expose your AI service internally within the cluster.
- `kong.Route` and `kong.ServiceArgs` define how external requests are routed to your AI service.
- Lastly, we export the constructed URL where the AI service can be accessed. Replace the placeholder domain with your actual domain.

Remember to adapt configurations (like image names, routes, and ports) to align with your own application's architecture and deployment strategy.