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Posted on Jun 10 • Originally published at towardsdev.com on Apr 29

Deploying OpenClaw: AI Agent Automation Stack on Google Cloud Platform — A Complete Step-by-Step…

#openclaw #gcp #agents #clouddeployment

Deploying OpenClaw: AI Agent Automation Stack on Google Cloud Platform — A Complete Step-by-Step Guide

AI agents are rapidly evolving from experimental tools into production-grade systems that can automate complex workflows, reason across multiple data sources, and interact with real-world applications. However, deploying a reliable, scalable AI agent stack often involves stitching together multiple components — models, infrastructure, networking, and security — which can quickly become overwhelming.

That’s where OpenClaw: AI Agent Automation Stack comes in. Designed to simplify the deployment and management of AI agents, OpenClaw provides an integrated environment for building, running, and scaling intelligent automation workflows. When combined with the power and flexibility of Google Cloud Platform (GCP), it enables developers and teams to launch production-ready AI systems with minimal setup overhead.

In this step-by-step guide, we’ll walk through how to deploy OpenClaw directly from the GCP Marketplace, configure your virtual machine, access the web interface, and start running AI-powered automation. Whether you’re a developer exploring agentic workflows or a team looking to operationalize AI, this guide will help you get up and running quickly and efficiently.

Step-by-Step Guide

Open OpenClaw: AI Agent Automation Stack listing on GCP Marketplace.
Click Get Started.

It will ask you to enable the API’s if they are not enabled already for your account. Please click on enable as shown in the screenshot.

It will take you to the agreement page. On this page, you can change the project from the project selector on the top navigator bar, as shown in the screenshot below.
Accept the Terms and agreements by ticking the checkbox and clicking on the AGREE button.

It will show you the successfully agreed popup page. Click on Deploy.

On the deployment page, give a name to your deployment.
In the Deployment Service Account section, click on the Existing radio button and choose a service account from the Select a Service Account dropdown.

If you don’t see any service account in the dropdown, then change the radio button to New Account and create the new service account here.

If, after selecting the New Account option, you get the permission error message below, please reach out to your GCP admin to create a service account by following the Step-by-step guide to create a GCP Service Account. Then refresh this deployment page once the service account is created; it should be available in the dropdown.

Select a zone where you want to launch the VM(such as us-east1-a)
Optionally change the number of cores and amount of memory.

Minimum VM Specs: 16GB RAM /4vCPU

This VM can also be deployed using an NVIDIA T4 GPU instance for faster inference. To deploy the VM with a GPU, click on the GPU tab as shown in the screenshot below and select a NVIDIA T4 GPU instance. Please note that GPU availability is limited to specific regions, zones, and machine types. If you do not see a GPU option for your selected region, zone, or machine type, try adjusting those settings to find available configurations.

Optionally change the boot disk type and size. (This defaults to ‘Standard Persistent Disk’ and 50GB, respectively)
Optionally change the network name and subnetwork names. Be sure that whichever network you specify exposes ports 22 (for SSH), 3389 (for RDP), and 443 (for HTTPS).
Click Deploy when you are done.
OpenClaw: AI Agent Automation Stack will begin deploying.

A summary page displays when the compute engine is successfully deployed. Click on the Instance link to go to the instance page .
On the instance page, click on the “SSH” button, select “Open in browser window”.

This will open an SSH window in a browser. Switch to the Ubuntu user and navigate to the Ubuntu home directory.

sudo su ubuntu

cd /home/ubuntu/

Run the command below to set the password for the “ubuntu” user

sudo passwd ubuntu

Now that the password for the Ubuntu user is set, you can connect to the VM’s desktop environment from any local Windows machine using RDP or a Linux machine using Remmina.
To connect using RDP via a Windows machine, first note the external IP of the VM from the VM details page, as highlighted below

Then, from your local Windows machine, go to the “Start” menu, in the search box type and select “Remote Desktop Connection.”
In the “Remote Desktop Connection” wizard, paste the external IP and click Connect

This will connect you to the VM’s desktop environment. Provide “ubuntu” as the username and the password set in step 6 to authenticate. Click OK

Now you are connected to the out-of-the-box OpenClaw: AI Agent Automation Stack VM’s desktop environment via Windows machines.

To connect using RDP via a Linux machine, first note the external IP of the VM from the VM details page, then from your local Linux machine, goto menu, in the search box type and select “Remmina”.

Note: If you don’t have Remmina installed on your Linux machine, first install Remmina as per your Linux distribution.

In the “Remmina Remote Desktop Client” wizard, select the RDP option from the dropdown and paste the external IP, and click Enter.

This will connect you to the VM’s desktop environment. Provide “ubuntu” as the username and the password set in step 6 to authenticate. Click OK

Now you are connected to the out-of-the-box OpenClaw: AI Agent Automation Stack VM’s desktop environment via a Linux machine.

The VM will generate a random password to log in to the OpenClaw Web Interface. To get the password, connect via SSH terminal as shown in the above steps and run the command below.

sudo cat /home/ubuntu/setup/.gateway_pass

To access the Open Claw Web Interface, copy the public IP address of the VM and paste it in your local browser as 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 17 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 an AI agent stack doesn’t have to be complex or time-consuming. With OpenClaw’s pre-configured environment and the robust infrastructure of Google Cloud Platform , you can move from setup to execution in a matter of minutes instead of days.

By following this guide, you’ve learned how to provision a VM, configure access via SSH and RDP, securely connect to the OpenClaw web interface, and even customize your AI models using tools like Ollama. More importantly, you now have a fully functional AI automation environment capable of handling real-world tasks — from document intelligence to multi-step agent workflows.

As you continue exploring OpenClaw, consider optimizing your deployment with GPU acceleration, experimenting with different LLMs, and integrating external tools or APIs to expand your agent’s capabilities. The real value lies not just in deployment, but in how you leverage these agents to automate, innovate, and scale your workflows.

Now that your system is live, the next step is simple: start building.