How to Deploy OpenClaw in Production: Docker, Kubernetes, and GPU Infrastructure

OpenClaw is an autonomous AI agent framework designed to execute tasks, orchestrate tools, and run continuously as a service.

Previously known as Clawdbot and Moltbot, the project has evolved into OpenClaw and is now gaining attention among developers building action-oriented AI systems.

While experimenting with OpenClaw locally is relatively straightforward, deploying OpenClaw in production environments introduces infrastructure considerations that teams must plan for carefully.

Because OpenClaw is built for persistent execution rather than one-time inference, production deployment requires a different architectural approach.

Why Production Deployment Is Different

Traditional AI applications often rely on stateless model inference. A request comes in, the model generates a response, and the process ends.

OpenClaw operates differently.

It is designed to:

• Run continuously
• Maintain state over time
• Orchestrate external tools
• Execute multi-step workflows

This means production environments must support:

• Persistent runtime execution
• Secure service exposure
• Reliable storage for logs and artifacts
• Controlled resource allocation
• Environment configuration management

OpenClaw behaves more like a long-running backend service than a simple prediction endpoint.

Deploying OpenClaw with Docker

OpenClaw is typically deployed as a containerized runtime.

Containerization allows teams to:

• Package the OpenClaw runtime and dependencies together
• Configure environment variables
• Manage exposed ports
• Mount persistent volumes
• Ensure consistent execution across environments

Because OpenClaw may expose services such as agent APIs or development interfaces, container networking must be configured deliberately.

Persistent volumes are often used to store:

• Agent state
• Logs
• Generated outputs

Using containers ensures reproducibility and simplifies moving from development to staging to production environments.

Deploying OpenClaw with Kubernetes

For teams running multiple services or requiring orchestration, Kubernetes provides additional control over OpenClaw deployments.

Kubernetes supports:

• Pod lifecycle management
• Automatic restarts
• Resource limits and allocation
• Secret management
• Persistent volume claims

Since OpenClaw is designed as a long-running service, orchestration ensures reliability and uptime in production.

When scaling agent-based systems, orchestration becomes increasingly important for maintaining stability and performance.

Does OpenClaw Require GPU Infrastructure?

OpenClaw itself does not strictly require a GPU.

However, GPU acceleration may become necessary depending on the underlying workload.

GPU resources are typically relevant when:

• Connecting to large language model backends
• Running embedding systems
• Handling vision-related tasks
• Performing compute-intensive reasoning steps

Because OpenClaw orchestrates models rather than functioning as the model itself, GPU requirements depend on what services the agent connects to.

This flexibility allows OpenClaw to operate in CPU-only environments or scale within GPU-backed infrastructure when required.

Infrastructure Considerations for Production

When deploying OpenClaw in production, teams should consider:

• Resource isolation for long-running services
• Secure port exposure
• Persistent storage for agent state
• Environment-based configuration
• Reliable restart mechanisms
• Optional GPU allocation based on workload

Agent systems introduce different operational requirements compared to simple inference APIs. Planning infrastructure accordingly reduces friction as usage scales.

Deploying OpenClaw with a Launch Template

Deploying OpenClaw from scratch requires configuring container environments, networking, persistent storage, environment variables, and optional GPU resources.

To simplify this process, OpenClaw is available as a launch template inside the Yotta Labs Console. This allows teams to deploy a preconfigured OpenClaw runtime environment without manually assembling the container infrastructure.

Instead of focusing on infrastructure setup, teams can concentrate on building and refining agent logic.

Final Thoughts

As AI systems shift from stateless response generation to persistent autonomous execution, deployment models must evolve as well.

OpenClaw represents this transition toward agent-centric architectures. It requires infrastructure that supports long-running execution, controlled resources, optional GPU acceleration, and container orchestration.

Understanding how to deploy OpenClaw properly is essential for teams building production-grade autonomous AI systems.

And as agent-based architectures continue to grow, deployment strategy becomes just as important as model capability.