AI is evolving from simple conversational models and becoming more autonomous. No longer are we limited by just conversational AI, but now we can install AI agents that can help with task completion, data processing, and in business as well. Many individuals and organizations are now wanting to host their AI agents for greater control of the performance of the agent, the privacy of their data, and the costs associated with hosting an agent.
One of the popular methods of hosting an AI agent is by using a Virtual Private Server (VPS). By utilizing both Ollama and OpenClaw, you can create your own AI agent.
Why Host Your AI Agents Yourself?
The benefits of self hosting your AI agents are numerous and provide an opportunity to create your own infrastructure rather than relying on a third party source or cloud service. By providing an isolated, reliable VPS for AI, you have the flexibility to host your own language models, automate your business processes, and implement agent base solutions in your own infrastructure. You may want to consider self hosting AI solutions when you require enhanced privacy, consistent performance, and the ability to modify the component of your technology stack at any time.
For those who are starting to experiment with AI, researchers, and individual developers, self hosting will provide the most reasonable option for continuing their exploration of AI.
Ollama and OpenClaw Overview
Ollama is a simple way to deploy and manage large language models (LLMs) while making it easier for people to run locally available open-source models, reducing reliance on public APIs, and allowing them to choose which model to use.
OpenClaw is used to build AI agents that can complete tasks, communicate with other devices and systems, and streamline operations through automation. Unlike traditional conversational assistants, OpenClaw can be more than just a conversational assistant; it can also execute other functions as needed to help users meet their goals.
Together, Ollama and OpenClaw function together by providing an application programming interface (API) for the runtime of the language model platform (Ollama); the runtime is transformed into a working AI agent (OpenClaw).
Select An Appropriate VPS(For Example, A Basic VPS)
In order to get started you must select infrastructure to support the expected workloads you will be using. As compared to typical web sites or enterprise applications, AI applications generally require more resources, therefore selecting the correct hardware based upon your development and testing requirements should be considered very carefully.
For your initial development and testing of AI applications (including many of the larger language models), a basic VPS configuration that has multiple CPUs with adequate SSD storage and at least 8GB of RAM, will help ensure you will have a good foundation from which to grow your AI application. For many developers and companies, using a cheap VPS hosting, and then increasing the resources as you build your applications, has become the strategy of choice.
Server Environment Configuration
Your virtual private server is now prepared for use, but the next step is to update the operating system and system packages that you may require. There are four aspects to consider in configuring the server:
Security Updates: Running system patches to protect the OS kernel.
Firewall Configuration: Restricting network access to essential ports only (such as SSH and the agent UI).
Non-Root User Creation: Operating via a sudo privileged user rather than the root account to mitigate security risks.
Containerization Runtime: Installing Docker or alternative engines required for clean dependency isolation.
Setting these things up together will create a secure, consistent operating environment from the outset.
Installation and Configuration of Ollama
After securing your VPS environment, the following phase involves installing Ollama along with an LLM customized for your hardware limitations. While larger language models generally provide superior reasoning skills, they demand much more computational capacity and introduce higher token latency when processed using a CPU bound architecture.
For a basic VPS installation with 8GB of RAM, running highly quantized models ensures the best performance without triggering out-of-memory (OOM) system crashes.
Action / Task |
Terminal Command (Copy & Paste) |
Expected Output / Notes |
| Install Ollama Service | curl -fsSL [https://ollama.com/install.sh](https://ollama.com/install.sh) | sh | Downloads and configures the Ollama background service automatically as a daemon on Linux. |
| Deploy 4-Bit Quantized Model | ollama run llama3:8b-instruct-q4_K_M | Pulls and runs Llama 3 (8B). Optimized to fit comfortably within an 8GB RAM threshold. |
| Verify Local API Status | curl http://localhost:11434/api/tags | Returns an HTTP 200 OK code with a JSON list of active models. Confirms OpenClaw is ready to connect. |
OpenClaw Deployment
With the Ollama runtime established, the final step involves deploying OpenClaw and connecting it to your local model endpoint. Key configuration steps during this phase include:
- Connecting the OpenClaw orchestration layer to the local Ollama API endpoint (http://localhost:11434).
- Defining systemic agent behaviors, tools, and procedural execution workflows.
- Configuring external service integrations, webhooks, and API permissions.
- Mapping out how the agent will break down, process, and execute multi-step tasks.
Once finalized, the autonomous agent can safely leverage the local language model to execute data processing and system management routines without exposing internal information to external networks.
Evaluation of Performances / Resources.
For successful implementation of AI the developer must regularly monitor progress. By keeping a watchful eye on CPU load, memory usage, available storage and response times of server resources one can often identify potential performance issues before they manifest into larger reliability issues. Another very important aspect of AI is that of AI analytics. By understanding how an individual model utilizes resources the developer has the ability to make an informed decision regarding model selection or scaling capabilities.
If those who would like additional information on performing their own deployment follow this guide on VPS and learn how to deploy OpenClaw onto a VPS.
Final Thoughts
Hosting both Ollama and OpenClaw on a VPS provides you with a powerful way to create AI driven workflows while giving you complete control of your resources (configurations & costs ) and the security of your data. Properly configured server components, adherence to security best practices, and consistent monitoring of system performance enable you to have a dependable base for today’s AI agents.
As the need for autonomous AI continues to expand, self hosting will offer ample opportunities to build, test, and deploy these solutions.
