How to Set Up and Deploy an OpenClaw AI Agent on a VPS A new guide walks users through deploying an OpenClaw AI agent on a virtual private server, balancing cost, availability, and privacy. The tutorial covers server configuration, system requirements, and integration with LLM providers, emphasizing the trade-offs between public and private AI services. How to Set Up and Deploy an OpenClaw AI Agent on a VPS AI agents have quickly gone from being a niche novelty to something people use every day. And the best part is you no longer need to be a tech wizard to run your own. Some trade-offs remain, though, and they mostly come down to cost, availability, and privacy. Take ready-made solutions, for instance. They work out of the box but cost more and hand your data to the provider. On the other hand, if you decide to host at home, you will get complete control, but you will also have to guarantee 24/7 uptime, a reliable connection, and secure configuration since a misconfigured OpenClaw is an open door into your home network . The cloud sits somewhere between the two. You own the config and data, while the provider handles uptime and connectivity. You also run inference wherever you like. The only real downside here is that you’ll be in charge of the setup. That’s exactly what this guide is for. It will walk you through the whole process step by step so that you end up with a fully functional OpenClaw AI agent in the cloud. Choosing the Server Configuration In most cases, we want to choose a virtual server region https://3hcloud.com/docs/virtual-servers.html as close to where the user is located as possible. For example, if you’re in the European Union, you can choose the WAW-2 region Poland . Below are the OpenClaw system requirements: Let’s explain these requirements briefly. OpenClaw depends fully on the installed version of Node.js http://node.js . In theory, Node.js 22.19 can be installed on Ubuntu 20.04, but this may introduce issues down the line, especially during updates. As for vCPU, while the main JavaScript execution loop is single-threaded, OpenClaw regularly performs many parallel tasks at once. This is why multiple cores are beneficial. If you’re running a single solo agent, the minimum recommended configuration should be enough. However, if you’re planning on creating a multi-agent system, RAM will be your most important resource. To keep the agents from competing for memory and interfering with each other’s performance, you’ll need 16 GB or more. The same goes for available disk storage. As it runs, the AI agent will accumulate logs, and you may notice pretty quickly you’re running low on space. Around 40 GB should be a comfortable starting point since it’s enough both for the operating system and for running OpenClaw long-term without needing frequent cleanups. Preliminary Preparation Before you start the installation of OpenClaw, we advise you to get all the other components in place. OpenClaw’s architecture is based on the AI agent being the connecting link between different systems, namely: - LLM providers - Channels and user interfaces - Sources of data and context - Tools and external services - Orchestration systems and infrastructure It may sound like a lot, but the core idea is pretty simple: normalizing communication between all these parts. OpenClaw gets the role of a universal translator, capable of taking data from different sources, adding context, choosing the right model, and calling the tools needed to complete a task. LLM Providers Large language models enable the AI agent to reason, make decisions, and act. When setting up OpenClaw, one of the first choices you’ll need to make is whether you want to use a public or a private service. With public options, like ChatGPT, Claude, Mistral, and similar, you can set everything up in no time and get strong performance, but you’ll be dependent on the service provider. It can randomly shut down at any moment and stop your agent from performing its tasks. A good example is the US Department of Commerce export directive of June 12, 2026, due to which Anthropic was forced to cut off access to Fable 5 and Mythos 5 for all users. A private service Ollama, LM Studio, or vLLM, for instance lets you run LLM inference on your own or on a rented GPU server. You can use publicly available developments such as OpenAI OSS GPT as models. This approach is usually more expensive, but at least you won’t be dependent on a service provider. Another good thing is that the data will be processed inside a closed perimeter and will not become available to the LLM provider. If you opt for a public service, though, you should keep in mind that some providers don’t allow you to start using API access without first subscribing to a pricing plan. That’s why you’ll need to set up billing before you can generate a corresponding API key. You’ll then give it to the agent during the onboarding process, thereby providing access to the computational resources. Each provider has its own setup page: OpenAI ChatGPT : https://platform.openai.com/api-keys https://platform.openai.com/api-keys Anthropic Claude : https://console.anthropic.com/settings/keys https://console.anthropic.com/settings/keys Google AI Studio Gemini : https://aistudio.google.com/apikey https://aistudio.google.com/apikey Mistral AI: https://console.mistral.ai/api-keys https://console.mistral.ai/api-keys OpenRouter: https://openrouter.ai/settings/keys https://openrouter.ai/settings/keys Writer’s note: At the time of writing this article, these were the 5 most popular providers. However, if you’d rather go down a private installation road, you’ll need to prepare an endpoint for connection in advance and create a key. These are the verification commands for the most popular self-hosted solutions: Ollama : curl http://