How does Cloud Batch differ from running jobs on GKE?

Cloud Batch provisions and deprovisions VMs automatically for each job, so you pay only for the duration of the job. GKE requires a running cluster with persistent node pools. Batch is ideal for jobs that run periodically or on-demand where you do not want to maintain infrastructure between runs. GKE is better for workloads that run continuously or need Kubernetes-specific features like service mesh, pod autoscaling, or complex scheduling constraints.

Can I use Spot VMs with Cloud Batch?

Yes. Cloud Batch natively supports Spot VMs, which cost 60-91% less than on-demand VMs. When a Spot VM is preempted, Batch automatically retries the affected tasks on new VMs according to your retry policy. Configure maxRetryCount and set the provisioning model to SPOT in the allocation policy. For fault-tolerant workloads, combining Spot VMs with Batch retry logic provides significant cost savings.

GCP Batch Job Builder

ComputeGCP

Build GCP Batch job configurations with task groups, container runnables, Spot VMs, and allocation policies.

Cloud Batch Configuration

Build GCP Batch job configurations with task groups, container runnables, Spot VMs, and allocation policies.

Required Fields

taskGroupstaskGroups[0].taskSpec.runnablestaskGroups[0].taskCountallocationPolicy

{
  "taskGroups": [
    {
      "taskSpec": {
        "runnables": [
          {
            "container": {
              "imageUri": "us-docker.pkg.dev/my-project/batch-jobs/data-processor:latest",
              "entrypoint": "/bin/sh",
              "commands": ["-c", "python3 /app/process.py --input=${BATCH_TASK_INDEX}"]
            },
            "environment": {
              "variables": {
                "INPUT_BUCKET": "gs://my-input-bucket",
                "OUTPUT_BUCKET": "gs://my-output-bucket"
              }
            }
          }
        ],
        "computeResource": {
          "cpuMilli": 4000,
          "memoryMib": 8192,
          "bootDiskMib": 50000
        },
        "maxRetryCount": 3,
        "maxRunDuration": "3600s",
        "volumes": [
          {
            "gcs": {
              "remotePath": "my-input-bucket"
            },
            "mountPath": "/mnt/input"
          }
        ]
      },
      "taskCount": 100,
      "parallelism": 10,
      "taskCountPerNode": 1
    }
  ],
  "allocationPolicy": {
    "instances": [
      {
        "policy": {
          "machineType": "e2-standard-4",
          "provisioningModel": "SPOT"
        }
      }
    ],
    "location": {
      "allowedLocations": ["regions/us-central1"]
    },
    "serviceAccount": {
      "email": "batch-sa@my-project.iam.gserviceaccount.com",
      "scopes": ["https://www.googleapis.com/auth/cloud-platform"]
    },
    "network": {
      "networkInterfaces": [
        {
          "network": "projects/my-project/global/networks/prod-vpc",
          "subnetwork": "projects/my-project/regions/us-central1/subnetworks/batch-subnet",
          "noExternalIpAddress": true
        }
      ]
    }
  },
  "logsPolicy": {
    "destination": "CLOUD_LOGGING"
  },
  "labels": {
    "team": "data-engineering",
    "env": "production"
  }
}

Generated Output

Output will appear here...

About This Tool

Google Cloud Batch is a fully managed service for scheduling and running batch processing workloads on Compute Engine VMs. It handles VM provisioning, task scheduling, queue management, and automatic retries, so you focus on your job logic rather than infrastructure. This builder helps you define batch job configurations including task specs, resource requirements, parallelism settings, retry policies, and VM provisioning models (on-demand or Spot), generating the JSON job definition and gcloud commands.

When to Use This Tool

•Configuring a batch job for processing thousands of genomics files in parallel using Spot VMs to minimize cost
•Building a multi-task batch job with dependencies between task groups — preprocessing, computation, and aggregation stages
•Setting up a GPU-accelerated batch job for ML inference that provisions N1 instances with NVIDIA T4 GPUs on demand
•Creating a batch job with custom container images, mounted Cloud Storage buckets, and automatic retry on preemption

Frequently Asked Questions

How does Cloud Batch differ from running jobs on GKE?: Cloud Batch provisions and deprovisions VMs automatically for each job, so you pay only for the duration of the job. GKE requires a running cluster with persistent node pools. Batch is ideal for jobs that run periodically or on-demand where you do not want to maintain infrastructure between runs. GKE is better for workloads that run continuously or need Kubernetes-specific features like service mesh, pod autoscaling, or complex scheduling constraints.
Can I use Spot VMs with Cloud Batch?: Yes. Cloud Batch natively supports Spot VMs, which cost 60-91% less than on-demand VMs. When a Spot VM is preempted, Batch automatically retries the affected tasks on new VMs according to your retry policy. Configure maxRetryCount and set the provisioning model to SPOT in the allocation policy. For fault-tolerant workloads, combining Spot VMs with Batch retry logic provides significant cost savings.

Related Learning Guides

Compute Engine Machine Types22 min read

Disclaimer: This tool runs entirely in your browser. No data is sent to our servers. Always verify outputs before using them in production. AWS, Azure, and GCP are trademarks of their respective owners.

GCP Batch Job Builder

ComputeGCP

Build GCP Batch job configurations with task groups, container runnables, Spot VMs, and allocation policies.

Cloud Batch Configuration

Build GCP Batch job configurations with task groups, container runnables, Spot VMs, and allocation policies.

Required Fields

taskGroupstaskGroups[0].taskSpec.runnablestaskGroups[0].taskCountallocationPolicy

Generated Output

Output will appear here...

About This Tool

When to Use This Tool

•Configuring a batch job for processing thousands of genomics files in parallel using Spot VMs to minimize cost
•Building a multi-task batch job with dependencies between task groups — preprocessing, computation, and aggregation stages
•Setting up a GPU-accelerated batch job for ML inference that provisions N1 instances with NVIDIA T4 GPUs on demand
•Creating a batch job with custom container images, mounted Cloud Storage buckets, and automatic retry on preemption

Frequently Asked Questions

How does Cloud Batch differ from running jobs on GKE?: Cloud Batch provisions and deprovisions VMs automatically for each job, so you pay only for the duration of the job. GKE requires a running cluster with persistent node pools. Batch is ideal for jobs that run periodically or on-demand where you do not want to maintain infrastructure between runs. GKE is better for workloads that run continuously or need Kubernetes-specific features like service mesh, pod autoscaling, or complex scheduling constraints.
Can I use Spot VMs with Cloud Batch?: Yes. Cloud Batch natively supports Spot VMs, which cost 60-91% less than on-demand VMs. When a Spot VM is preempted, Batch automatically retries the affected tasks on new VMs according to your retry policy. Configure maxRetryCount and set the provisioning model to SPOT in the allocation policy. For fault-tolerant workloads, combining Spot VMs with Batch retry logic provides significant cost savings.

Related Learning Guides

Compute Engine Machine Types22 min read