When it comes to running an OpenClaw server, choosing the right hosting setup is key to ensuring stability, security, and cost-efficiency. This guide explores your options for hosting OpenClaw and which server solution works best for a range of scenarios.

What server options are available for hosting OpenClaw?

OpenClaw is quite flexible regarding where it can run. It supports the following operating systems:

  • Linux kernel 5.4 or newer
  • macOS 13 and up
  • Windows 10 and newer

Linux distributions like Ubuntu, Debian, and Fedora have been extensively tested and perform well in production environments. This wide compatibility means that OpenClaw can be run either locally or on almost any standard data server. Here are some options for hosting:

  • Bare metal servers: Dedicated physical servers offer the best performance and give you full control over hardware and configuration. They’re ideal for high-security production environments or resource-heavy AI agents.
  • Cloud VMs: Virtual machines in the cloud provide flexible scaling and quick provisioning. Resources can be adjusted as needed, which is perfect for dynamic workloads.
  • Virtual Private Server (VPS): VPS offer dedicated resources at a lower cost. They’re great for smaller or experimental setups and are easy to manage, but scaling options are more limited compared to cloud VMs.
  • Container platforms: Contains like Docker and Kubernetes offer portable, modular deployments. They’re a good fit for scalable systems and automated DevOps environments.
Note

OpenClaw can access sensitive data, including system files and credentials. Since third-party integrations may contain malware, running OpenClaw in an isolated environment (using Docker containers) is recommended. This approach keeps processes separate and helps minimise risks from API keys and prevent data leaks.

Is it better to host your OpenClaw server at home or in a data centre?

Choosing between hosting OpenClaw on your own network or in a data centre comes down to your specific use case, workload, and desired level of professionalism.

Home hosting

Running your own server might sound appealing: full control, no cloud fees and complete data sovereignty. For a simple OpenClaw server setup with an external language model, basic hardware often does the trick: 2–4 GB of RAM and 1–2 vCPUs are enough for testing or occasional use. But if you want to run local models or expect heavy traffic, your CPU, RAM and GPU requirements increase significantly, along with the costs:

  • For stable continuous operation: Around 2–4 vCPUs and 8–16 GB RAM with 40–80 GB SSD storage will be enough for long sessions, multiple integrations and moderately intensive workloads in production environments.
  • For production or team setups: A good baseline would be 4 vCPUs or more, 16–64 GB of RAM and a fast SSD.
  • For high-end setups and local models: Running large local models or using GPU acceleration requires dedicated GPUs or high-memory systems. This is typically done with specialised hardware or cloud GPU instances.

Besides computing power, network connectivity is key: Outgoing HTTPS connections (port 443) are required and for external access, you’ll need incoming connections with a domain and TLS. Power costs also add up with 24/7 operation. Depending on your hardware, power usage can range from 20 to 40 watts for energy-efficient systems to over 100 watts for high-performance setups. Annual electricity costs can vary from double to triple-digit amounts. Don’t forget about additional factors like cooling, maintenance, spare parts, and the time you spend managing everything.

Advantages Disadvantages
Full data sovereignty High initial investment for high-performance hardware
No ongoing cloud rental fees Ongoing power costs for 24/7 operation
Freedom to customise Requires maintenance and admin work
No dependency on cloud providers No professional failover protection
Good for experimental setups Scaling only possible through hardware upgrades

Hosting in a data centre

Data centre servers are available for a monthly fee and often cost no more than running an energy-efficient home server. They also come without upfront costs, maintenance or risk of downtime. This option is great if your OpenClaw server needs to always be online – like for bots, webhooks or internal automation. Data centres offer redundant power, stable network connections and fixed IP addresses. Backups, snapshots, and scaling are also usually easy to set up.

If your setup requires more resources, cloud solutions with containerisation or orchestration, such as an OpenClaw Docker installation are a good fit. These setups grow as needed but can get pricier depending on redundancy and demand. You’re paying not just for computing power but for reliability, scalability, and easier management. Dedicated bare-metal servers in a data centre are best for consistent, high-demand workloads, GPU use, or regulatory compliance.

Advantages Disadvantages
High availability and redundancy Ongoing monthly costs
No need to purchase hardware Dependent on the provider
Easy scaling Less physical control
Static IP and professional infrastructure Costs increase with high load or GPU needs
Backups and snapshots often included Possible contract or plan commitments

What are the hosting requirements for different OpenClaw tiers?

In a side-by-side comparison, the differences become clear. While simple VPS setups cost about the same as a home server’s electricity, professional-grade setups are much more expensive, but offer predictable availability, scalability and reliability. Your decision ultimately comes down to the level of security, availability, and management you need for your OpenClaw server.

Level Typical scenario Home hosting – required hardware & computing power Server hosting – typical solution
Minimal Simple text agents with external LLM, few users 1–2 CPU cores, 4–8 GB RAM, no GPU needed. A low-power desktop or small business computer is sufficient Small cloud VM or VPS with 2 vCPUs and 4 GB RAM
Optimal Agents with image analysis, multiple integrations, or local models up to ~7–13B Multiple CPU cores (e.g., modern i5/Ryzen 5 class), 16–32 GB RAM, optional GPU with 8–12 GB VRAM for local models Larger cloud instance (4–8 vCPUs, 8–16 GB RAM) or containerised environment
High-end Multi-agent systems for enterprises, high parallelism, large local models Powerful multicore CPU, 64 GB+ RAM, dedicated GPU with 24 GB+ VRAM or multiple GPUs Scalable cloud architecture or bare-metal server, possibly GPU server
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