AWS Batch

Name: AWS Batch API
Brand: AWS Batch
Availability: InStock

★ Only Publicly Available OpenAPI DocumentCloud InfrastructureComputeapiKey24 EndpointsREST

For Agents

Submit, monitor, and cancel containerised batch jobs across managed compute environments and queues, with support for array jobs, dependencies, and Spot-backed scaling.

Quickstart

Get started with AWS Batch in minutes using your preferred integration method.

# Add to your MCP client config (Claude Desktop, Cursor, Windsurf)
{
  "jentic": {
    "url": "https://api.jentic.com/mcp",
    "auth": "oauth"
  }
}

# Then ask your agent:
"submit an AWS Batch job"

# → Jentic returns the GET /events tool with parameter schema, agent executes.

Capabilities

What an agent can do with AWS Batch API.

Register job definitions that describe the container image, vCPU, memory, environment variables, and IAM role for a batch job

Create managed or unmanaged compute environments backed by EC2 Spot, EC2 On-Demand, or Fargate with min/max vCPU bounds

Create job queues that map to one or more compute environments and apply priority and fair-share scheduling policies

Submit jobs, array jobs (up to 10,000 children), and jobs with dependsOn relationships to other jobs

GET STARTED

Start building with AWS Batch API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI document for AWS Batch, keeping it validated and agent-ready.

AWS Batch runs containerised batch computing workloads at any scale by dynamically provisioning the right amount and type of compute on EC2 or Fargate. The API lets you register job definitions, create compute environments backed by EC2 Spot, On-Demand, or Fargate capacity, define job queues with priority and scheduling policies, submit jobs with array and dependency support, and inspect or cancel jobs as they run. It is well suited to genomics pipelines, financial Monte Carlo, media transcoding, and other parallel container workloads.

Use Cases

Patterns agents use AWS Batch API for, with concrete tasks.

★ Massively Parallel Array Job

Run a 5,000-task simulation, transcode, or analytics sweep without managing servers. Register a job definition with the container image and vCPU/memory request, then SubmitJob with arrayProperties.size=5000. AWS Batch fans out 5,000 child jobs onto the compute environment, retries failures based on the retry strategy, and writes per-job logs to CloudWatch Logs. Spot capacity keeps the run cheap.

SubmitJob with jobName=monte-carlo-2026-06-09, jobDefinition=monte-carlo:7, jobQueue=hpc-spot, and arrayProperties={size: 5000}, then poll DescribeJobs every 60 seconds until status=SUCCEEDED.

Pipeline with Job Dependencies

Express a multi-stage pipeline (preprocess to analyse to post-process) by chaining jobs with dependsOn relationships. Each stage's SubmitJob call references the prior stage's job ID; AWS Batch holds dependent jobs in PENDING until the parent reaches SUCCEEDED. Suitable for ETL, genomics, and any directed-acyclic workflow that fits inside Batch's scheduler without needing Step Functions.

Submit job preprocess (returns id A), then submit job analyse with dependsOn=[{jobId: A}], then submit post-process with dependsOn=[{jobId: <analyse-id>}], all into queue analytics-prod.

Cost-Optimised Fargate Spot

Run short-lived containerised batch workloads on Fargate Spot to cut compute spend by up to 70 percent. Create a managed compute environment with type=FARGATE_SPOT and a maxvCpus cap, attach it to a job queue, and submit jobs as normal. AWS Batch handles capacity reclaim by re-queuing interrupted jobs on the next available Fargate Spot capacity.

CreateComputeEnvironment with type=MANAGED, computeResources.type=FARGATE_SPOT, maxvCpus=256, and subnets=[subnet-xxx], then CreateJobQueue mapping to it with priority=1.

Agent-Driven Job Operator

Let an agent take a research request such as 'rerun yesterday's failed sequencing batches with double the retry budget' and translate it into AWS Batch operations. The agent calls Jentic to list failed jobs, derive the array indices that need retry, and submit a new array job pointing at those indices. AWS credentials stay in the Jentic vault.

Search Jentic for 'list failed AWS Batch jobs', execute it filtered to jobQueue=hpc-spot and status=FAILED in the last 24 hours, then SubmitJob with the failed indices and retryStrategy.attempts=4.

Key Endpoints

24 endpoints — aws batch runs containerised batch computing workloads at any scale by dynamically provisioning the right amount and type of compute on ec2 or fargate.

METHOD

PATH

DESCRIPTION

POST

/v1/submitjob

Submit a job, array job, or dependent job

POST

/v1/describejobs

Describe one or more jobs by ID

POST

/v1/listjobs

List jobs in a queue with filters

POST

/v1/canceljob

Cancel a job that is not yet running

POST

/v1/terminatejob

Terminate a running job

POST

/v1/registerjobdefinition

POST

/v1/createcomputeenvironment

Create a compute environment

POST

/v1/createjobqueue

Create a job queue

POST

/v1/submitjob

Submit a job, array job, or dependent job

POST

/v1/describejobs

Describe one or more jobs by ID

POST

/v1/listjobs

List jobs in a queue with filters

POST

/v1/canceljob

Cancel a job that is not yet running

POST

/v1/terminatejob

Terminate a running job

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

AWS access key ID and secret access key for AWS Batch are stored encrypted in the Jentic vault. Agents receive scoped, short-lived signing credentials and the raw IAM secrets never enter the agent context. Jentic computes the AWS Signature Version 4 signature server-side for every request.

Intent-based discovery

Agents search Jentic by intent (for example, 'submit a batch job') and Jentic returns matching AWS Batch operations with their input schemas, the correct AWS service endpoint, and the required IAM action, so the agent can invoke the right call without crawling the AWS docs.

Time to first call

Direct AWS Batch integration: 1-3 days for AWS SDK setup, IAM role configuration, Sigv4 signing, and error handling. Through Jentic: under 1 hour, search, load schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Complementary

Amazon EC2

Provides the underlying compute that AWS Batch managed compute environments provision.

Use EC2 directly when the agent needs custom AMIs or networking; use AWS Batch when scheduling many containerised jobs across managed capacity.

Alternative

AWS Lambda

Serverless functions for short, request-scoped work where Batch's queue-and-container model is too heavy.

Choose Lambda for tasks under 15 minutes that fit the Lambda runtime; choose Batch for long-running, parallel containerised compute.

Alternative

Amazon EKS

Kubernetes-native batch via Argo Workflows, Volcano, or Kueue when the team already runs EKS.

Choose EKS-based batch when the workload must share a Kubernetes platform with other services; choose AWS Batch for a managed, queue-first scheduler.

FAQs

Specific to using AWS Batch API through Jentic.

What authentication does the AWS Batch API use?

All requests are signed with AWS Signature Version 4 using an AWS access key ID and secret access key. Through Jentic, the credentials live encrypted in the vault and Jentic performs the signing server-side, so the agent never sees the raw secret.

Can AWS Batch run jobs on Fargate instead of EC2?

Yes. Create a managed compute environment with computeResources.type=FARGATE or FARGATE_SPOT and AWS Batch will run jobs as Fargate tasks instead of EC2 instances. Fargate is well suited to short-lived jobs that need fast start times without the EC2 instance bootstrap delay.

How do I submit a 1,000-task array job through Jentic?

Search Jentic for 'submit an AWS Batch job', load the SubmitJob schema, and execute it with arrayProperties={size: 1000}. The operation maps to POST /v1/submitjob. Jentic returns the parent job ID; each child carries an index in AWS_BATCH_JOB_ARRAY_INDEX so the container can fetch its work shard.

What are the rate limits for the AWS Batch API?

Standard AWS service throttling applies; high SubmitJob rates can return ThrottlingException. AWS recommends batching submissions through array jobs (up to 10,000 children per call) rather than calling SubmitJob in tight loops.

Can I express job dependencies in AWS Batch?

Yes. Pass dependsOn=[{jobId: <parent-id>}] when calling SubmitJob and the new job stays PENDING until the parent reaches SUCCEEDED. This supports linear pipelines and small DAGs natively without needing AWS Step Functions.

Is AWS Batch free to use?

AWS Batch itself has no separate charge. You pay only for the EC2 or Fargate compute, the EBS or ephemeral storage, and the CloudWatch Logs the jobs consume.