When should I use Fargate vs EC2 for AWS Batch?

Use Fargate for most batch workloads — it eliminates the need to manage EC2 instances, scales to zero when idle, and provisions resources per-job. Use EC2 when you need GPU instances, custom AMIs, higher vCPU/memory ratios than Fargate supports, placement groups for tightly coupled HPC workloads, or Amazon Linux instances with specific kernel configurations. EC2 also makes sense when you have sustained workloads where reserved instances reduce costs compared to Fargate's per-second pricing.

How do retry strategies work in Batch job definitions?

The retry strategy specifies the number of times Batch should retry a failed job (1-10 attempts). You can also configure evaluateOnExit rules that examine the exit code, status reason, or error message to decide whether to retry or fail immediately. For example, you might retry on exit code 1 (transient error) but fail immediately on exit code 137 (out of memory) since retrying would produce the same result. Each retry runs a fresh container instance.

AWS Batch Job Definition Builder

ComputeAWS

Build AWS Batch job definitions for Fargate and EC2 with container configs.

AWS Batch Configuration

Build AWS Batch job definition configs with container properties, retry strategies, and timeouts.

Required Fields

jobDefinitionNametypecontainerProperties.imagecontainerProperties.resourceRequirements

{
  "jobDefinitionName": "data-pipeline-processor",
  "type": "container",
  "platformCapabilities": ["FARGATE"],
  "containerProperties": {
    "image": "123456789012.dkr.ecr.us-east-1.amazonaws.com/data-processor:latest",
    "resourceRequirements": [
      {
        "type": "VCPU",
        "value": "4"
      },
      {
        "type": "MEMORY",
        "value": "8192"
      }
    ],
    "jobRoleArn": "arn:aws:iam::123456789012:role/BatchJobRole",
    "executionRoleArn": "arn:aws:iam::123456789012:role/ecsTaskExecutionRole",
    "command": ["python", "process.py", "--input", "Ref::inputPath"],
    "environment": [
      {
        "name": "ENV",
        "value": "production"
      },
      {
        "name": "LOG_LEVEL",
        "value": "INFO"
      }
    ],
    "secrets": [
      {
        "name": "DB_PASSWORD",
        "valueFrom": "arn:aws:secretsmanager:us-east-1:123456789012:secret:prod/db-password"
      }
    ],
    "logConfiguration": {
      "logDriver": "awslogs",
      "options": {
        "awslogs-group": "/aws/batch/job",
        "awslogs-region": "us-east-1",
        "awslogs-stream-prefix": "data-processor"
      }
    },
    "networkConfiguration": {
      "assignPublicIp": "DISABLED"
    },
    "fargatePlatformConfiguration": {
      "platformVersion": "LATEST"
    }
  },
  "retryStrategy": {
    "attempts": 3,
    "evaluateOnExit": [
      {
        "onStatusReason": "Host EC2*",
        "action": "RETRY"
      },
      {
        "onExitCode": "1",
        "action": "EXIT"
      }
    ]
  },
  "timeout": {
    "attemptDurationSeconds": 3600
  },
  "tags": {
    "Project": "data-pipeline",
    "Environment": "production"
  }
}

Generated Output

Output will appear here...

About This Tool

AWS Batch simplifies running batch computing workloads by automatically provisioning compute resources and scheduling jobs. A job definition specifies the container image, vCPU and memory requirements, environment variables, mount points, retry strategies, and timeout settings. With support for both Fargate and EC2 compute environments, you need to choose the right platform capabilities and configure resource requirements accordingly. The Batch Job Definition Builder generates complete job definition JSON with proper container properties, retry logic, and platform-specific configurations.

When to Use This Tool

•Defining Fargate-based batch jobs with proper vCPU/memory combinations and ephemeral storage settings
•Creating EC2-based job definitions with GPU requirements for machine learning training workloads
•Configuring multi-node parallel job definitions for MPI-based high-performance computing workloads
•Building job definitions with retry strategies and timeout settings that handle transient failures gracefully

Frequently Asked Questions

When should I use Fargate vs EC2 for AWS Batch?: Use Fargate for most batch workloads — it eliminates the need to manage EC2 instances, scales to zero when idle, and provisions resources per-job. Use EC2 when you need GPU instances, custom AMIs, higher vCPU/memory ratios than Fargate supports, placement groups for tightly coupled HPC workloads, or Amazon Linux instances with specific kernel configurations. EC2 also makes sense when you have sustained workloads where reserved instances reduce costs compared to Fargate's per-second pricing.
How do retry strategies work in Batch job definitions?: The retry strategy specifies the number of times Batch should retry a failed job (1-10 attempts). You can also configure evaluateOnExit rules that examine the exit code, status reason, or error message to decide whether to retry or fail immediately. For example, you might retry on exit code 1 (transient error) but fail immediately on exit code 137 (out of memory) since retrying would produce the same result. Each retry runs a fresh container instance.

Related Learning Guides

ECS vs EKS Decision Guide24 min read

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