Load balancers are important for distributing incoming network traffic across multiple backend services to ensure high availability and reliability. In a dynamic environment, you might want to automate load balancer configurations as part of your CI/CD processes. Here, we'll explore how you can automate GCP Load Balancer configurations with CTO.ai during deployments.


Set Up Google Cloud Service Account and Your Environment

Create a service account on GCP to give CTO.ai access:

  • Go to GCP console > IAM & admin > Service accounts.
  • Create a new service account with permissions for Compute Admin to mamange Load Balancers.
  • Download the JSON key for this account.

Configure and Set up GCP Workflows

Before we get started with this guide, install the GCP GKE Pulumi Py workflow from. If you don't have access to the repository, kindly contact us at [email protected] The repo includes a complete IaC for deploying infrastructure over GCP: Kubernetes, Container Registry, Database Clusters, Load Balancers, and Project Resource Management, all built using Python + Pulumi + CTO.ai.

Clone the repository with:

git clone “https://github.com/workflows-sh/gcp-gke-pulumi-py.git” 

Cd gcp-gke-pulumi-py

Run and Set up your Infrastructure

Next, you need to build and set up the infrastructure that will deploy each resource to GCP using the GCP workflow stack. Set up your infrastructure using the ops run -b . This will provision your stack and set up your infrastructure.

  • Select setup infrastrcture over GCP
  • This process will build your Docker image and start provisioning your GCP infra resources.
  • Next, select the services you want to deploy from the CLI. We will select the all service and install all the dependencies, which will also provision our GCP container registry.
  • Back in the GCP console, click on your container registry, and you will see your Container Registry created for usage.

CTO.ai Configuration

In your project ops.yml file we will have something similar to:

version: "1"
  - name: sample-expressjs-pipeline-gcp-gke-pulumi-pyf:0.1.1
    description: Build and Publish an image in a GCPContainer Registry
        - DEBIAN_FRONTEND=noninteractive
        - STACK_TYPE=gcp-gke-pulumi-py
        - ORG=cto-ai
        - GH_ORG=workflows-sh
        - REPO=sample-expressjs-gcp-gke-pulumi-py
        - BIN_LOCATION=/tmp/tools
        - GITHUB_TOKEN
        - PULUMI_TOKEN
      - "github:workflows-sh/sample-gcp-gke-pulumi-py:pull_request.opened"
      - "github:workflows-sh/sample-gcp-gke-pulumi-py:pull_request.synchronize"
      - "github:workflows-sh/sample-gcp-gke-pulumi-py:pull_request.merged"
      - name: sample-expressjs-build-gcp-gke-pulumi-py
        description: Build step for sample-expressjs-gcp-gke-pulumi-py
          - git
          - unzip
          - wget
          - tar
          - mkdir -p $BIN_LOCATION
          - export PATH=$PATH:$BIN_LOCATION
          - ls -asl $BIN_LOCATION

  - name: update load balancer 
    description: set up load balancer and utils 
      - echo $GCLOUD_SERVICE_KEY | gcloud auth activate-service-account --key-file=-
      - gcloud config set compute/zone YOUR_COMPUTE_ZONE
      - gcloud config set project YOUR_PROJECT_ID

  - name: update-loadbalancer
      - ./path-to-your/script.sh

Scripting Load Balancer Updates

Your script.sh config mentioned above would include the necessary gcloud commands to update your Load Balancer. Here’s a config you can use, you can update it to suite your needs like:


# Ensure the script stops on first error
set -e

# Update backend services or targets
gcloud compute backend-services update BACKEND_SERVICE_NAME --description "updated via CI/CD"

# Add or remove instances from instance groups if required
gcloud compute instance-groups managed add-instances INSTANCE_GROUP_NAME --instances=INSTANCE_NAMES

# Any other required configurations
# ...

echo "Load Balancer configuration updated!"

Testing Your Setup

Before deploying to production, use can set up a test environment to verify that all workloads and resources are working perfectly using the chmod +x script.sh command locally or you can deploy it using the ops run -b . command. Before making any changes, always back up your current load balancer configurations, and set up monitoring and analytics using CTO.ai insights.


Automating your GCP Load Balancer configurations as part of your CI/CD pipeline in CTO.ai can enhance deployments and ensure consistency across environments. With CTO.ai and the combination with GCP, you can achieve efficient, automated, and error-free deployments.