AWS Batch vs Google Cloud Batch

AWS Batch plans, schedules, and runs containerized batch workloads for machine learning, simulation, and analytics. It provisions and scales compute on Amazon ECS, Amazon EKS, and AWS Fargate, with Spot and On-Demand instance options. Jobs can be submitted through the AWS Management Console, command line interfaces, or software development kits, and specify memory and vCPU requirements. Queues support priorities, dependencies, retries, and scheduling based on resource needs. Jobs can request GPUs, which Batch can scale for and isolate to the appropriate containers. For high-communication workloads, it supports multi-node parallel jobs across EC2 instances and Elastic Fabric Adapter. Workflow integrations include Pegasus WMS, Luigi, Nextflow, Metaflow, Apache Airflow, and AWS Step Functions. The console shows compute capacity and job metrics; job logs appear in the console and Amazon CloudWatch Logs. AWS Batch runs in the cloud and supports API, Linux, macOS, web, and Windows access. The service itself has no additional charge, but compute and storage resources used to store and run jobs are billed separately. Jobs need to be executable as Docker containers. Security follows a shared responsibility model: AWS protects cloud infrastructure, while customers are responsible for security in their own cloud use.

Google Cloud Batch schedules, queues, and runs batch jobs on Google infrastructure. It obtains resources for jobs, creates or removes them during runtime to match job specifications, and can scale compute resources automatically for parallel work. Jobs run executable scripts or containerized workloads, with tasks arranged sequentially or in parallel. Storage options include persistent disks, local SSDs, Cloud Storage buckets, and network file systems such as Filestore. Integrations identified for a developer workflow include Cloud Storage, Pub/Sub, Cloud Logging, and Workflows. Batch can send job and task state changes through Pub/Sub, with notifications streamable to BigQuery. It supports IAM permissions and VPC Service Controls; Secret Manager can provide sensitive job data. Batch itself has no additional charge, but resources used by jobs are billed, and quotas and limits apply. Each job is restricted to one task group, named group0, and finished jobs are automatically deleted after 60 days unless deleted earlier. It is available through API and web, with cloud deployment.