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Deploy a pod to assign hardware resources to a browser automation experiment. Once deployed, the automation is published and available in the Browser Automation node within workflows, where all defined variables and configuration parameters can be accessed. Deployment also supports versioning, approvals, validation checks, and rollback mechanisms to promote workflows and agents safely from development to staging and production.
With Browser Automation Deployment, you can:
  • Deploy a pod for a browser automation experiment.
  • Customize resource allocation for deployments.
  • Set auto-scaling limits to ensure optimal performance.
  • Review deployed and ready-to-deploy pods.
  • View details of the latest and previous deployment versions.
  • Redeploy pods that were previously undeployed.
  • Undeploy or delete pods.
  • Search for a deployment.
Billing and credit usage for Browser Automation are calculated based on the deployed pods and hardware configuration.

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Access Browser Automation Deployment

  1. Log in to AI for Process.
  2. In the top menu, click Settings.
  3. On the left navigation menu, click Browser Automation > Deployment.

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Deployment Dashboard

The Deployment page shows the following information for each pod:
FieldDescription
Deployment nameName assigned to the deployment.
DescriptionBrief description of the deployment.
StatusCurrent status: Deploying, Deployed, or Ready to Deploy.
Created byUser who created the deployment pod.
Updated onDate the deployment was last updated.

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Deploy a Pod

Prerequisite: Ensure your account has sufficient credits to deploy a pod.
  1. Click Deploy in the top-right corner.
  2. In General Settings, enter the Deployment name and Description.
  3. In Resource allocation, configure the following: Scaling parameters:
    ParameterDescription
    Min replicasMinimum number of pods per service. Recommended: 1.
    Max replicasMaximum number of pods per service. Recommended: 1.
    Auto-scaling thresholdWhen average compute load exceeds this percentage, the system automatically adds more pods to handle increased demand. Recommended: 75%.
    Hardware: Select a hardware profile (number of vCPUs) from the dropdown.
    Selecting a hardware profile displays the hourly credit consumption, profiling data, and the number of browser automations that can run in parallel on that hardware. Additional requests are queued.
  4. Review the deployment details and accept the terms.
  5. Click Deploy to proceed. You can also save the configuration as a draft or go back to modify details.
A success message confirms that the pod is deployed. A confirmation email is also sent when the deployment completes.

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View Deployment Details

Click a deployment entry to see the following tabs:
TabDescription
OverviewShows the configured general settings and resource allocation.
Deployment HistoryLists all deployment and undeployment versions by date and time. The latest version is marked with a green check icon. Click the expansion icon to view duration, start and end times, deployment time, and status.
ConfigurationsShows the deployment name and description. Provides options to undeploy or delete the pod.
Deployment history
Delete permanently removes the pod and its data from all experiments where it is deployed. Only a Master Admin or Workspace Owner can delete a pod. Proceed with caution — this action cannot be undone.

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Undeploy a Pod

Undeploying a pod temporarily disassociates it from all browser experiments. You can redeploy it later.
  1. On the Deployment page, click the pod you want to undeploy.
  2. Click Configurations on the left menu.
  3. In the Danger Zone section, click Proceed to Undeploy.
  4. Click Un-deploy in the confirmation window.
A success message confirms the undeployment. A confirmation email is also sent.

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Redeploy a Pod

You can redeploy an active or undeployed pod and optionally update the general and resource allocation settings before redeployment.
  1. On the Deployment page, click the pod you want to redeploy.
  2. Click Re-deploy in the top-right corner, then confirm in the dialog.
  3. Modify any required parameters. See Deploy a Pod for configuration steps.
  4. Click Deploy on the Review page.
A success message confirms the deployment. A confirmation email is also sent. The status shows Deploying while in progress and changes to Deployed once complete.