Imagine being able to run AI-assisted coding assistance in a scalable, cloud-native environment where resources scale automatically and you pay only for what you use. That's exactly what running Claude Code on Kubernetes enables—and I'm going to walk you through every single step, even if you've never touched containers before.

In this guide, I'll show you how to deploy Claude Code within a Kubernetes cluster, giving your development team access to powerful AI coding assistance without managing individual installations. This setup is perfect for teams wanting centralized AI tooling, cost optimization, and consistent environments across projects.

What You'll Need Before Starting

Gather these prerequisites before we begin. Don't worry if some of these sound unfamiliar—I'll explain each one briefly:

Understanding the Architecture

Before diving into code, let's visualize what we're building. The architecture consists of three main layers working together:

The beauty of this setup is that the API gateway can handle authentication, rate limiting, and request logging—all while using HolySheep's infrastructure which offers <50ms latency and supports WeChat/Alipay payments at ¥1=$1 (saving 85%+ compared to ¥7.3 rates).

Step 1: Creating the Kubernetes Configuration

Let's start by creating the configuration files. I'll walk you through each one carefully.

Creating the Namespace

First, we'll create a dedicated namespace for our Claude Code deployment. This keeps everything organized and isolated from other services in your cluster.

# namespace.yaml
apiVersion: v1
kind: Namespace
metadata:
  name: claude-code
  labels:
    app: claude-code
    environment: development

Apply this configuration using kubectl:

kubectl apply -f namespace.yaml

You should see output confirming the namespace was created successfully. Think of namespaces like folders in a file system—they help organize resources logically.

Step 2: Building the Claude Code API Gateway

Now we need to create a service that acts as an intermediary between your Claude Code client and HolySheep AI's API. This gateway will handle authentication, logging, and request transformation.

# claude-gateway-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: claude-gateway
  namespace: claude-code
  labels:
    app: claude-gateway
spec:
  replicas: 2
  selector:
    matchLabels:
      app: claude-gateway
  template:
    metadata:
      labels:
        app: claude-gateway
    spec:
      containers:
      - name: gateway
        image: nginx:alpine
        ports:
        - containerPort: 8080
        volumeMounts:
        - name: config
          mountPath: /etc/nginx/nginx.conf
          subPath: nginx.conf
        - name: api-keys
          mountPath: /etc/secrets
          readOnly: true
      volumes:
      - name: config
        configMap:
          name: nginx-config
      - name: api-keys
        secret:
          secretName: holysheep-api-key
---
apiVersion: v1
kind: Service
metadata:
  name: claude-gateway
  namespace: claude-code
spec:
  type: LoadBalancer
  selector:
    app: claude-gateway
  ports:
  - port: 80
    targetPort: 8080
    protocol: TCP

Step 3: Configuring the Nginx Reverse Proxy

The heart of our gateway is the Nginx configuration that routes requests to HolySheep AI's API. This is where we define the proxy rules and header transformations.

# nginx-configmap.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: nginx-config
  namespace: claude-code
data:
  nginx.conf: |
    worker_processes auto;
    error_log /var/log/nginx/error.log warn;
    pid /var/run/nginx.pid;
    
    events {
      worker_connections 1024;
    }
    
    http {
      include /etc/nginx/mime.types;
      default_type application/octet-stream;
      
      log_format main '$remote_addr - $remote_user [$time_local] "$request" '
                      '$status $body_bytes_sent "$http_referer" '
                      '"$http_user_agent" "$http_x_forwarded_for"';
      
      access_log /var/log/nginx/access.log main;
      
      sendfile on;
      keepalive_timeout 65;
      
      # Claude Code API proxy
      server {
        listen 8080;
        
        location /v1/chat/completions {
          proxy_pass https://api.holysheep.ai/v1/chat/completions;
          proxy_http_version 1.1;
          proxy_set_header Host api.holysheep.ai;
          proxy_set_header Content-Type application/json;
          proxy_set_header Authorization "Bearer ${HOLYSHEEP_API_KEY}";
          proxy_buffering off;
          proxy_request_buffering off;
        }
        
        location /v1/completions {
          proxy_pass https://api.holysheep.ai/v1/completions;
          proxy_http_version 1.1;
          proxy_set_header Host api.holysheep.ai;
          proxy_set_header Content-Type application/json;
          proxy_set_header Authorization "Bearer ${HOLYSHEEP_API_KEY}";
          proxy_buffering off;
        }
        
        location /health {
          return 200 'OK';
          add_header Content-Type text/plain;
        }
      }
    }

Step 4: Setting Up Secrets and Environment Variables

Security is paramount. We store our API key as a Kubernetes Secret, which encrypts it at rest and limits access to authorized pods only.

# Create the secret (replace YOUR_HOLYSHEEP_API_KEY with your actual key)
kubectl create secret generic holysheep-api-key \
  --from-literal=HOLYSHEEP_API_KEY=YOUR_HOLYSHEEP_API_KEY \
  --namespace=claude-code


Verify the secret was created

kubectl get secrets -n claude-code

Important: Never commit your actual API key to version control. Use environment variables or Kubernetes secrets instead. The secret we just created will be injected into our gateway pods at runtime.

Step 5: Deploying to Your Cluster

Now let's deploy everything to your Kubernetes cluster. I'll provide both the complete deployment script and individual steps.

# Complete deployment script - save as deploy.sh and run with bash deploy.sh

#!/bin/bash
set -e

NAMESPACE="claude-code"
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"

echo "Creating namespace..."
kubectl apply -f "$SCRIPT_DIR/namespace.yaml"

echo "Applying ConfigMap..."
kubectl apply -f "$SCRIPT_DIR/nginx-configmap.yaml"

echo "Deploying gateway..."
kubectl apply -f "$SCRIPT_DIR/claude-gateway-deployment.yaml"

echo "Waiting for pods to be ready..."
kubectl wait --for=condition=ready pod -l app=claude-gateway \
  -n "$NAMESPACE" --timeout=120s

echo "Checking deployment status..."
kubectl get pods -n "$NAMESPACE"
kubectl get svc -n "$NAMESPACE"

echo "Getting external IP..."
kubectl get svc -n "$NAMESPACE" claude-gateway -o jsonpath='{.status.loadBalancer.ingress[0].hostname}'

echo "Deployment complete!"

Step 6: Configuring Your Claude Code Client

With the gateway deployed, you now need to configure Claude Code to route its requests through our new endpoint. Here's how to set that up.

# .claude-code-settings.json
{
  "api_base_url": "http://claude-gateway.claude-code.svc.cluster.local/v1",
  "model": "claude-sonnet-4-20250514",
  "max_tokens": 4096,
  "temperature": 0.7,
  "timeout_ms": 30000
}


Alternative: Environment variable configuration

export CLAUDE_API_BASE_URL="http://claude-gateway.claude-code.svc.cluster.local/v1"
export CLAUDE_MODEL="claude-sonnet-4-20250514"

Testing Your Setup

Let's verify everything is working correctly. I'll show you how to run a simple test that confirms your gateway can communicate with HolySheep AI's API.

# Test script - save as test-gateway.sh

#!/bin/bash

GATEWAY_URL="http://claude-gateway.claude-code.svc.cluster.local"

echo "Testing health endpoint..."
curl -s "$GATEWAY_URL/health" && echo "" || echo "Health check failed"

echo ""
echo "Testing chat completions API..."
curl -s -X POST "$GATEWAY_URL/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4-20250514",
    "messages": [{"role": "user", "content": "Hello, respond with just the word SUCCESS if you can read this."}],
    "max_tokens": 50
  }' | jq -r '.choices[0].message.content' 2>/dev/null || echo "API test failed"

echo ""
echo "Testing completion endpoint..."
curl -s -X POST "$GATEWAY_URL/v1/completions" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4.1",
    "prompt": "Write a single line: HELLO_WORLD",
    "max_tokens": 20
  }' | jq -r '.choices[0].text' 2>/dev/null || echo "Completion test failed"

If all tests pass, you should see SUCCESS responses from both endpoints. The gateway is now routing your requests to HolySheep AI's infrastructure, where you'll benefit from their competitive pricing: GPT-4.1 at $8/MTok, Claude Sonnet 4.5 at $15/MTok, and DeepSeek V3.2 at just $0.42/MTok.

Implementing Rate Limiting

For production deployments, you'll want to implement rate limiting to prevent abuse and control costs. Here's an enhanced Nginx configuration that includes rate limiting.

# Enhanced nginx configuration with rate limiting

Add this to your nginx.conf within the http block


http {
  # Rate limiting zones
  limit_req_zone $binary_remote_addr zone=api_limit:10m rate=10r/s;
  limit_conn_zone $binary_remote_addr zone=conn_limit:10m;
  
  server {
    # Apply rate limiting
    limit_req zone=api_limit burst=20 nodelay;
    limit_conn conn_limit 10;
    
    # Request logging with cost tracking
    log_format cost_tracking '$remote_addr - $remote_user [$time_local] '
                             '"$request" $status $body_bytes_sent '
                             'cost=$request_length upstream=$upstream_addr';
    
    location /v1/chat/completions {
      # Existing proxy configuration...
      proxy_pass https://api.holysheep.ai/v1/chat/completions;
      proxy_set_header Host api.holysheep.ai;
      proxy_set_header Authorization "Bearer ${HOLYSHEEP_API_KEY}";
      
      # Custom headers for cost tracking
      proxy_set_header X-Request-Start $msec;
    }
  }
}

Monitoring and Observability

Running AI services without monitoring is like flying blind. I recommend setting up Prometheus metrics to track API usage, latency, and costs. Here's a simple metrics endpoint you can add to your gateway.

In my hands-on experience deploying this setup across three different development teams, I found that adding detailed logging upfront saved hours of debugging later. The investment in observability pays off quickly when you're trying to optimize token usage and identify bottlenecks.

Common Errors and Fixes

Even with careful setup, you'll likely encounter some issues. Here are the most common problems and their solutions:

Error 1: "Connection Refused" from Claude Code Client

This typically means the gateway service isn't reachable from your client. The most common cause is incorrect service DNS resolution.

# Diagnosis: Check if the service exists and has endpoints
kubectl get svc -n claude-code
kubectl get endpoints -n claude-code claude-gateway


If endpoints are missing, check pod status

kubectl get pods -n claude-code
kubectl describe pod -n claude-code -l app=claude-gateway


Common fix: Delete and recreate the deployment

kubectl delete deployment claude-gateway -n claude-code
kubectl apply -f claude-gateway-deployment.yaml


Verify pods are running

kubectl rollout status deployment/claude-gateway -n claude-code

Error 2: "401 Unauthorized" from HolySheep AI

This indicates your API key isn't being passed correctly through the proxy. This is often due to environment variable substitution issues in the ConfigMap.

# First, verify the secret exists and contains the key
kubectl get secret holysheep-api-key -n claude-code -o yaml


Check if the key is correctly referenced in nginx config


Note: ConfigMaps don't support direct secret interpolation



Solution: Use an init container to inject the API key


Update your deployment spec:

spec:
  initContainers:
  - name: init
    image: busybox:1.36
    command: ['sh', '-c', 'echo $HOLYSHEEP_API_KEY > /etc/nginx/secrets/api_key.txt']
    env:
    - name: HOLYSHEEP_API_KEY
      valueFrom:
        secretKeyRef:
          name: holysheep-api-key
          key: HOLYSHEEP_API_KEY
    volumeMounts:
    - name: api-key-file
      mountPath: /etc/nginx/secrets
  containers:
  - name: gateway
    # Update nginx config to read from file instead of env var
    env:
    - name: HOLYSHEEP_API_KEY
      valueFrom:
        secretKeyRef:
          name: holysheep-api-key
          key: HOLYSHEEP_API_KEY

Error 3: "504 Gateway Timeout" on Large Requests

Long responses or large prompts can exceed default timeout settings. You'll need to adjust both Nginx and Kubernetes timeout configurations.

# Update nginx.conf with extended timeouts
location /v1/chat/completions {
  proxy_pass https://api.holysheep.ai/v1/chat/completions;
  proxy_http_version 1.1;
  
  # Timeout settings
  proxy_connect_timeout 60s;
  proxy_send_timeout 300s;
  proxy_read_timeout 300s;
  
  # Buffer settings for streaming
  proxy_buffering off;
  proxy_cache off;
  chunked_transfer_encoding on;
  proxy_set_header Connection '';
  
  # Required headers
  proxy_set_header Host api.holysheep.ai;
  proxy_set_header Authorization "Bearer ${HOLYSHEEP_API_KEY}";
}


Also update your Service to have longer timeout annotations

apiVersion: v1
kind: Service
metadata:
  name: claude-gateway
  namespace: claude-code
  annotations:
    service.kubernetes.io/aws-load-balancer-backend-protocol: http
spec:
  type: LoadBalancer
  ports:
  - port: 80
    targetPort: 8080
    timeoutSeconds: 300

Error 4: Pods Stuck in CrashLoopBackOff

This usually indicates the container is failing to start. Common causes include missing dependencies or configuration errors.

# Check pod logs for the actual error
kubectl logs -n claude-code -l app=claude-gateway --previous


Common fix: Ensure nginx config is valid YAML syntax


Check for issues like:


- Incorrect indentation


- Missing colons after keys


- Invalid characters in values



If the ConfigMap was malformed, fix and reapply

kubectl delete configmap nginx-config -n claude-code
kubectl apply -f nginx-configmap.yaml


Delete stuck pods to force recreation

kubectl delete pods -n claude-code -l app=claude-gateway


Monitor the new pods starting correctly

kubectl logs -n claude-code -l app=claude-gateway -f

Cost Optimization Tips

Using HolySheep AI through this Kubernetes setup gives you significant cost advantages. Their pricing structure is notably competitive: with a rate of ¥1=$1, you're saving 85%+ compared to typical ¥7.3 rates. You can pay easily via WeChat or Alipay, and the <50ms latency means faster response times.

Here are strategies I use to minimize costs:

Production Readiness Checklist

Before moving to production, verify you've addressed these critical items:

Conclusion

You've now learned how to deploy a complete Claude Code infrastructure on Kubernetes, complete with API gateway, authentication handling, and production-ready configurations. This setup gives your team centralized access to AI coding assistance while leveraging HolySheep AI's competitive pricing and excellent latency.

The combination of Kubernetes orchestration and HolySheep's infrastructure provides a scalable, cost-effective solution that grows with your team's needs. Whether you're a solo developer or managing a large engineering organization, this architecture scales from a single-node Minikube cluster to multi-region production deployments.

Remember to start small, test thoroughly, and iterate based on your team's actual usage patterns. The flexibility of this architecture means you can always adjust configurations as requirements evolve.

If you encountered any issues following this guide or want to share your own tips, the HolySheep community is a great place to connect with other developers building with AI tooling.

👉 Sign up for HolySheep AI — free credits on registration

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