What is AWS Batch?
AWS Batch is a fully managed batch computing service. You define jobs with container images and resource requirements, and Batch automatically provisions optimal compute infrastructure, starts jobs, and scales based on workload.
The service eliminates manual cluster management and automatically optimizes costs through Spot instance usage.
Core Features
- Automatic Scaling: Compute capacity grows and shrinks with workload
- Job Dependencies: Complex workflows with job dependencies and arrays
- Spot Integration: Automatic use of Spot instances for cost savings
- Container-based: Jobs run in Docker containers from ECR
- Priority Queues: Different queues for different workload priorities
Typical Use Cases
Scientific Computing: Simulations and calculations requiring thousands of parallel jobs. Batch automatically scales to required capacity and uses Spot instances for cost efficiency.
Financial Modeling: Risk calculations and Monte Carlo simulations with high compute requirements. Job arrays enable parallel execution of thousands of scenarios.
Media Processing: Video encoding, rendering, and image processing at scale. GPU instances accelerate compute-intensive workloads.
Benefits
- No cluster management required
- Automatic cost optimization with Spot instances
- Unlimited scaling for workload peaks
- Pay-per-use without base fees
Integration with innFactory
As an AWS Reseller, innFactory supports you with AWS Batch: job definition, workflow orchestration, cost optimization with Spot instances, and integration into existing pipelines.
Typical Use Cases
Frequently Asked Questions
What does AWS Batch cost?
AWS Batch itself is free. You only pay for the compute resources used: EC2 instances, Fargate containers, or Spot instances. Spot instances can be up to 90% cheaper than On-Demand.
Which compute options does Batch support?
EC2 On-Demand, EC2 Spot, AWS Fargate, and Fargate Spot. Batch automatically selects the optimal instance type mix based on job requirements.
Can I use GPUs for ML workloads?
Yes. Batch supports GPU instances (P3, P4, G4) for machine learning, rendering, and scientific computing. GPU requirements are specified in job definitions.
How does Batch differ from Lambda?
Lambda for short event-driven functions (max 15 min). Batch for long-running jobs with container images, arbitrary runtime, and complex dependencies between jobs.