https://public_ip_of_vm. Make sure to use https and not http.
The browser will display an SSL certificate warning message. Expand the warning message, accept the certificate warning, and continue.
- It will open a login page. Provide the password we got at step 14 above and click connect.
- After the connection is successful, it will ask you to pair the device. For that, go back to the SSH terminal and run the following command to get the request ID.
openclaw devices list
- To approve the request, replace the request ID obtained in the previous step in the command below.
openclaw devices approve <requestId>
e.g openclaw devices approve 31b187f9–4545–44fc-bf49-dd1553753b25
Note: Sometimes the request may expire if there is a delay in approving it. If the request gets rejected, simply rerun the "openclaw devices list" command to obtain a new request ID, and then run the approve command using that ID. Also, each browser profile generates a unique device ID, so switching browsers or clearing browser data will require re-pairing. And lastly, if your VM's IP address is dynamic, which changes on VM reboot, then you also need to repair your device.
- Now you are logged in to the OpenClaw Web Interface. You can set up your Agent, configure various channels, and start the automation.
- By default, the LLM model set is "gpt-oss:20b". You can pull other ollama models and switch them to primary models by running the commands below.
ollama pull <modelname>
e.g ollama pull llama3.1:8b
openclaw models set ollama/llama3.1:8b
- Once the model is switched, go back to the Web Interface, refresh the page, and select the new model from the model dropdown. Once the model is loaded successfully, you can run your queries.
For more details, please visit the Official Documentation page
Conclusion
Deploying OpenClaw on Microsoft Azure unlocks a powerful combination of flexibility, scalability, and control for building AI-driven automation systems. With just a few setup steps, you gain access to a fully configured environment where you can run agent workflows, integrate LLMs, and experiment with multi-agent architectures.
From provisioning the VM and configuring secure access to connecting via SSH/RDP and setting up models, the process is straightforward once you understand each component. The ability to switch models, leverage GPU instances, and manage agents through a web interface makes OpenClaw especially valuable for both prototyping and production use cases.
As AI agents continue to redefine how software interacts with the world, tools like OpenClaw provide the foundation to build smarter, more autonomous systems. Now that your environment is ready, the next step is to start experimenting — create agents, connect integrations, and push the boundaries of what automated intelligence can do.
Thank you so much for reading
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