Claude Code Docker Compose Production (2026)

Claude Code Docker Compose Production Guide

Running Claude Code in a Docker Compose environment offers significant advantages for teams seeking consistent AI assistant behavior across multiple workstations or CI/CD pipelines. This guide covers practical deployment patterns, configuration strategies, and production-ready examples that work with modern development workflows.

Why Docker Compose for Claude Code

Docker Compose simplifies container orchestration for multi-service applications. When you containerize Claude Code, you gain reproducible environments, easy scaling, and straightforward dependency management. Many developers use this approach to ensure every team member works with identical Claude Code configurations, eliminating the “it works on my machine” problems that plague collaborative projects.

Beyond consistency, containerizing Claude Code solves a real operational problem: API key management. In a shared team environment, every developer needs their own key or a shared service account key. Docker Compose lets you inject secrets at runtime through environment variables or .env files, keeping credentials out of your codebase while still giving each container the access it needs.

The setup works particularly well when combined with other containerized development tools. For instance, pairing Claude Code with a frontend-design skill container lets you generate consistent UI prototypes across your entire team without installing Node.js dependencies locally.

Basic Docker Compose Setup

Create a docker-compose.yml file in your project directory:

version: '3.8'
services:
 claude-code:
 image: anthropic/claude-code:latest
 container_name: claude-code-dev
 volumes:
 - ./workspace:/workspace
 - claude-config:/root/.claude
 environment:
 - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
 stdin_open: true
 tty: true
 restart: unless-stopped
volumes:
 claude-config:

Start the container with:

docker-compose up -d
docker-compose exec claude-code claude --help

This basic setup mounts a local workspace folder and persists Claude configuration across restarts. The environment variable approach keeps your API key secure while making the container portable.

For interactive sessions, attach to the running container:

docker-compose exec claude-code bash
Then inside the container:
claude "explain the architecture of the files in /workspace/src"

The claude-config named volume persists your Claude settings, installed skills, and conversation history between container restarts. Without it, every docker-compose down would reset your configuration.

Production Configuration with Multiple Services

Production deployments often require more sophisticated setups. Here’s a practical example that combines Claude Code with supporting services:

version: '3.8'
services:
 claude-code:
 image: anthropic/claude-code:latest
 container_name: claude-code-prod
 volumes:
 - ./projects:/workspace
 - ./skills:/root/.claude/skills
 - claude-state:/data
 environment:
 - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
 - CLAUDE_MODEL=claude-opus-4-6
 - LOG_LEVEL=info
 networks:
 - claude-network
 restart: always
 healthcheck:
 test: ["CMD", "claude", "--version"]
 interval: 30s
 timeout: 10s
 retries: 3
 # Supporting service for document generation
 pdf-generator:
 build: ./pdf-service
 container_name: pdf-service
 networks:
 - claude-network
 volumes:
 - ./output:/app/output
networks:
 claude-network:
 driver: bridge
volumes:
 claude-state:

This configuration demonstrates several production best practices. The health check ensures container health monitoring works correctly with your orchestration platform. Dedicated networks isolate services while allowing controlled communication. The persistent volume preserves state between deployments.

Key Production Settings Explained

Setting Value Why It Matters
restart: always Always restart on failure Ensures Claude Code recovers from crashes or OOM events
healthcheck claude --version every 30s Allows load balancers to route away from unhealthy instances
CLAUDE_MODEL Explicit model ID Prevents unexpected behavior when new model versions release
Named network claude-network Isolates Claude Code traffic from other application services
Named volume claude-state Survives docker-compose down without losing configuration

Integrating Claude Skills in Docker

One powerful pattern involves pre-loading Claude skills into your container. The pdf skill works excellently in containerized environments where you need programmatic PDF generation:

services:
 claude-code:
 # ... base config ...
 volumes:
 - ./skills:/root/.claude/skills
 - /var/run/docker.sock:/var/run/docker.sock

Mounting the Docker socket lets Claude Code manage sibling containers for tasks like generating PDFs on demand or running temporary build environments. This approach pairs well with tdd workflows where you want automated test execution in isolated containers.

To pre-install skills at container build time rather than mounting them from the host, write a custom Dockerfile:

FROM anthropic/claude-code:latest
Pre-install skills so the container starts ready
RUN claude skill install pdf && \
 claude skill install tdd && \
 claude skill install frontend-design
Set working directory
WORKDIR /workspace

Then reference your custom image in Compose:

services:
 claude-code:
 build:
 context: .
 dockerfile: Dockerfile.claude
 container_name: claude-code-prod

This pattern is useful for team environments where you want all developers to have the same skill set without requiring each person to run installation commands manually.

Environment-Specific Configurations

Different environments require different configurations. Use Docker Compose overrides for environment-specific settings:

docker-compose.yml (base):

services:
 claude-code:
 image: anthropic/claude-code:latest

docker-compose.override.yml (development):

services:
 claude-code:
 volumes:
 - ./workspace:/workspace
 environment:
 - DEBUG=true

docker-compose.prod.yml (production):

services:
 claude-code:
 restart: always
 logging:
 driver: "json-file"
 options:
 max-size: "10m"
 max-file: "3"

Apply production settings with:

docker-compose -f docker-compose.yml -f docker-compose.prod.yml up -d

This override pattern keeps your base configuration DRY while allowing each environment to customize exactly what it needs. Development gets verbose logging and a local workspace mount. Production gets log rotation and restart policies. Staging can sit in between, inheriting the base and adding only its specific overrides.

For CI/CD pipelines, create a dedicated docker-compose.ci.yml:

services:
 claude-code:
 environment:
 - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
 - CI=true
 volumes:
 - ${GITHUB_WORKSPACE:-./}:/workspace
 restart: "no"

The restart: "no" ensures the container exits cleanly after the CI job completes rather than restarting and consuming runner credits.

Security Considerations

Production deployments require attention to security. Never commit API keys to version control. Use Docker secrets or environment files that are excluded from git:

.gitignore
.env
.env.local
*.env

Create a .env.example file instead that documents required variables without values:

.env.example. copy to .env and fill in values
ANTHROPIC_API_KEY=
CLAUDE_MODEL=claude-opus-4-6
LOG_LEVEL=info

For teams using secrets management tools, integrate with Vault or AWS Secrets Manager at startup:

services:
 claude-code:
 entrypoint: >
 /bin/sh -c "
 export ANTHROPIC_API_KEY=$(vault kv get -field=api_key secret/claude) &&
 exec claude-code
 "

Additional security hardening for the container itself:

services:
 claude-code:
 security_opt:
 - no-new-privileges:true
 read_only: true
 tmpfs:
 - /tmp
 user: "1000:1000"

Setting read_only: true with a tmpfs mount for /tmp prevents the container from writing to its filesystem layer, which reduces the blast radius if the process is compromised. Running as a non-root user (1000:1000) prevents privilege escalation attacks.

For enhanced security, consider running Claude Code in an isolated network namespace and using a reverse proxy for any exposed ports. The supermemory skill can help maintain secure conversation context without persisting sensitive data to disk.

Monitoring and Logging

Production Claude Code deployments benefit from structured logging and monitoring. The simplest approach uses JSON file logging with rotation:

services:
 claude-code:
 logging:
 driver: "json-file"
 options:
 max-size: "10m"
 max-file: "5"
 labels: "service,env"
 labels:
 service: "claude-code"
 env: "production"

For centralized log aggregation with Fluentd:

services:
 claude-code:
 logging:
 driver: "fluentd"
 options:
 fluentd-address: localhost:24224
 tag: "claude-code.{{.Name}}"

Container orchestration platforms like Kubernetes can further enhance monitoring with custom metrics and automatic restarts on failure.

To add basic uptime monitoring without a full observability stack, use the built-in health check with a notification script:

services:
 claude-code:
 healthcheck:
 test: ["CMD", "claude", "--version"]
 interval: 30s
 timeout: 10s
 retries: 3
 start_period: 10s

Combine this with a Docker event listener that fires alerts to Slack or PagerDuty when the container transitions to unhealthy.

Scaling Considerations

When scaling Claude Code across multiple instances, consider the stateless nature of the container. Each instance maintains its own conversation context, so implement session affinity if your workflow requires consistent context. A load balancer with sticky sessions handles this effectively:

services:
 load-balancer:
 image: nginx:alpine
 volumes:
 - ./nginx.conf:/etc/nginx/nginx.conf:ro
 ports:
 - "8080:80"
 depends_on:
 - claude-code-1
 - claude-code-2
 claude-code-1:
 image: anthropic/claude-code:latest
 environment:
 - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
 networks:
 - claude-network
 claude-code-2:
 image: anthropic/claude-code:latest
 environment:
 - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
 networks:
 - claude-network
networks:
 claude-network:
 driver: bridge

The corresponding nginx.conf uses ip_hash to implement sticky sessions:

upstream claude_backend {
 ip_hash;
 server claude-code-1:8080;
 server claude-code-2:8080;
}
server {
 listen 80;
 location / {
 proxy_pass http://claude_backend;
 }
}

For horizontal scaling beyond two instances, use Docker Compose’s --scale flag combined with a more sophisticated load balancer:

docker-compose up -d --scale claude-code=4

Note that --scale requires removing the explicit container_name field from your service definition, since multiple containers cannot share a name.

Debugging Production Issues

When a production Claude Code container behaves unexpectedly, these commands help narrow down the problem:

Check container status and exit codes
docker-compose ps
Tail logs from the last 100 lines
docker-compose logs --tail=100 -f claude-code
Inspect resource usage
docker stats claude-code-prod
Run a one-off diagnostic command inside the container
docker-compose exec claude-code claude --version
Check which skills are installed
docker-compose exec claude-code claude skill list
Verify environment variables reached the container
docker-compose exec claude-code env | grep ANTHROPIC

If the container is crash-looping, temporarily override the restart policy to prevent it from restarting and obscuring log output:

docker update --restart=no claude-code-prod
docker start claude-code-prod
docker logs --tail=200 claude-code-prod

Conclusion

Docker Compose provides a solid foundation for deploying Claude Code in production environments. The configuration patterns shown here, from basic single-container setups to multi-service architectures, scale from individual developer workstations to enterprise deployments. Combine these patterns with skills like frontend-design, pdf, and tdd to build powerful, containerized AI-assisted development workflows that your entire team can rely on.

Start with the basic configuration, add production hardening as needed, and use Docker Compose’s flexibility to adapt your setup to evolving project requirements. The override file pattern in particular pays off quickly once you are managing more than one environment, a small upfront investment in file organization prevents significant configuration drift over time.

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