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Deploy to Kubernetes

This guide covers deploying containerized Strands agents to Kubernetes using Kind (Kubernetes in Docker) for local and cloud development.

Prerequisites

Step 1: Setup Kind Cluster

Create a Kind cluster: 

kind create cluster --name my-cluster

Verify cluster is running: 

kubectl get nodes

Step 2: Create Kubernetes Manifests

The following assume you have completed the Docker deployment guide with the following file structure:

Project Structure (Python): 

my-python-app/
├── agent.py                # FastAPI application (from Docker tutorial)
├── Dockerfile              # Container configuration (from Docker tutorial)
├── pyproject.toml          # Created by uv init
└── uv.lock                 # Created automatically by uv

Project Structure (TypeScript): 

my-typescript-app/
├── index.ts                # Express application (from Docker tutorial)
├── Dockerfile              # Container configuration (from Docker tutorial)
├── package.json            # Created by npm init
├── tsconfig.json           # TypeScript configuration
└── package-lock.json       # Created automatically by npm

Add k8s-deployment.yaml to your project: 

apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
spec:
  replicas: 1
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      containers:
      - name: my-app
        image: my-image:latest
        imagePullPolicy: Never
        ports:
        - containerPort: 8080
        env:
        - name: OPENAI_API_KEY
          value: "<your-api-key>"
---
apiVersion: v1
kind: Service
metadata:
  name: my-service
spec:
  selector:
    app: my-app
  ports:
  - port: 8080
    targetPort: 8080
  type: NodePort

This example k8s-deployment.yaml uses OpenAI, but any supported model provider can be configured. See the Strands documentation for all supported model providers. For instance, to include AWS credentials:

env:
  - name: AWS_ACCESS_KEY_ID
    value: "<your-access-key-id>"
  - name: AWS_SECRET_ACCESS_KEY
    value: "<your-secret-access-id>"
  - name: AWS_REGION
    value: "us-east-1"

Step 3: Deploy to Kubernetes

Build and load your Docker image: 

docker build -t my-image:latest .
kind load docker-image my-image:latest --name my-cluster

Apply the Kubernetes manifests: 

kubectl apply -f k8s-deployment.yaml

Verify deployment: 

kubectl get pods
kubectl get services

Step 4: Test Your Deployment

Port forward to access the service: 

kubectl port-forward svc/my-service 8080:8080

Test the endpoints: 

# Health check
curl http://localhost:8080/ping

# Test agent invocation
curl -X POST http://localhost:8080/invocations \
  -H "Content-Type: application/json" \
  -d '{"input": {"prompt": "What is artificial intelligence?"}}'

Step 5: Making Changes

When you modify your code, redeploy with:

# Rebuild image
docker build -t my-image:latest .

# Load into cluster
kind load docker-image my-image:latest --name my-cluster

# Restart deployment
kubectl rollout restart deployment my-app

Cleanup

Remove the Kind cluster when done: 

kind delete cluster --name my-cluster

Optional: Deploy to Cloud-Hosted Kubernetes

Once your application works locally with Kind, you can deploy it to any cloud-hosted Kubernetes cluster.

See our documentation for Deploying Strands Agents to Amazon EKS as an example.

Step 1: Push Container to Repository

Push your image to a container registry: 

# Tag and push to your registry (Docker Hub, ECR, GCR, etc.)
docker tag my-image:latest <registry-url>/my-image:latest
docker push <registry-url>/my-image:latest

Step 2: Update Deployment Configuration

Update k8s-deployment.yaml for cloud deployment:

# Change image pull policy from:
imagePullPolicy: Never
# To:
imagePullPolicy: Always

# Change image URL from:
image: my-image:latest
# To:
image: <registry-url>/my-image:latest

# Change service type from:
type: NodePort
# To:
type: LoadBalancer

Step 3: Apply to Cloud Cluster

# Connect to your cloud cluster (varies by provider)
kubectl config use-context <cloud-context>

# Deploy your application
kubectl apply -f k8s-deployment.yaml

Additional Resources