Batch API

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

✓ Official Vendor SpecCloud InfrastructureComputeoauth29 EndpointsREST

For Agents

Submit, list, and cancel Google Cloud Batch jobs that run containerised or script-based tasks on managed Compute Engine and GKE.

Quickstart

Get started with Batch API 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 a Google Cloud Batch job"

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

Capabilities

What an agent can do with Batch API API.

Submit a Batch job that runs one or more tasks via POST /v1/{+parent}/jobs

List jobs in a project and location with GET /v1/{+parent}/jobs and filter by state

Cancel a running job via /v1/{+name}:cancel to stop further task execution

Report task state from inside a running task via /v1/{+parent}/state:report for custom task progress

GET STARTED

Start building with Batch API API

Explore with Jentic

View OpenAPI Document

Use for: I need to submit a Batch job that runs a Docker container across 100 tasks, List all Batch jobs in europe-west1 and their current state, Cancel a running Batch job that has produced enough results, Find every Batch location available to my project

Not supported: Does not store task results, manage long-running services, or run interactive workloads — use for submitting and monitoring batch jobs on managed Compute Engine and GKE only.

The Google Cloud Batch API runs batch compute jobs on managed Compute Engine and GKE infrastructure without requiring users to operate their own scheduler. It exposes operations to submit jobs that bundle one or more tasks, list and cancel running jobs, and report task state from inside the job. Batch handles VM provisioning, retries, and result collection so HPC, genomics, simulation, and rendering workloads can be expressed as a single API call. The API is the orchestration surface only and does not store result artifacts.

Use Cases

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

★ HPC and Simulation Workloads

Run high-performance computing, simulation, or rendering jobs as a single submission against a managed scheduler that handles VM provisioning, retries, and task coordination. Submit a job with the desired task count and resource shape; Batch boots the right machines, runs each task, and tears the fleet down on completion.

POST a Batch job spec to /v1/{parent}/jobs with taskCount=1024 and the desired machine type, then poll the operation until done.

Genomics and Scientific Pipelines

Express genomics or scientific pipelines as Batch jobs that fan out across many input samples, with each task running the same container against a different input. Suitable for sequencing variant calling, image processing pipelines, and large-scale data preprocessing without operating a Slurm or Kubernetes cluster directly.

Submit a Batch job whose runnable is a genomics container, with one task per sample listed in a Cloud Storage manifest.

Cost-Aware Job Cancellation

Watch a running job and cancel it once enough samples have produced acceptable results, capping cost on long-tail tasks. The API exposes both job listing and a cancel endpoint, so a controller running on a separate node can drive cost-aware execution policies.

POST to /v1/{name}:cancel once a downstream signal indicates enough successful task outputs have been collected.

AI Agent-Driven Batch Submission via Jentic

Use an AI agent to translate a high-level analysis request into a Batch job spec, submit it, and monitor it to completion. Through Jentic the agent finds Batch operations by intent and runs them with project credentials supplied at execute time, avoiding manual SDK setup in the agent runtime.

Use Jentic search for 'submit a Google Batch job', load the schema for POST /v1/{parent}/jobs, and execute it with the constructed job spec.

Key Endpoints

9 endpoints — the google cloud batch api runs batch compute jobs on managed compute engine and gke infrastructure without requiring users to operate their own scheduler.

METHOD

PATH

DESCRIPTION

POST

/v1/{+parent}/jobs

Create a Batch job with task definitions

GET

/v1/{+parent}/jobs

List Batch jobs in a project and location

GET

/v1/{+name}

Get a Batch job by resource name

DELETE

/v1/{+name}

Delete a Batch job

POST

/v1/{+name}:cancel

Cancel a running Batch job

POST

/v1/{+parent}/state:report

Report task state from inside a running task

GET

/v1/{+name}/locations

List Batch locations available to the project

POST

/v1/{+parent}/jobs

Create a Batch job with task definitions

GET

/v1/{+parent}/jobs

List Batch jobs in a project and location

GET

/v1/{+name}

Get a Batch job by resource name

DELETE

/v1/{+name}

Delete a Batch job

POST

/v1/{+name}:cancel

Cancel a running Batch job

Why though Jentic?

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

Credential isolation

Service account credentials are stored encrypted in the Jentic vault. Agents call Batch through Jentic with short-lived access tokens minted on demand and never see the JSON key.

Intent-based discovery

Agents search by intent (e.g. 'submit a Google Batch job', 'cancel a running job') and Jentic returns the matching Batch operation with its input schema, avoiding the discovery document.

Time to first call

Direct integration: 1-2 days for IAM setup, machine type validation, and operation polling. Through Jentic: under 1 hour once the credential is in the vault.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Complementary

Compute Engine API

Backs Batch with the underlying VMs that run jobs

Use Compute Engine when the agent needs full control over VM lifecycle rather than a managed batch scheduler.

Alternative

Dataflow API

Streaming and batch data processing as managed pipelines

Choose Dataflow when the workload is a data-transformation DAG rather than a fan-out of independent tasks.

Alternative

Kubernetes Engine API

Run batch workloads on a self-managed GKE cluster with custom controllers

Choose GKE when the team already runs Kubernetes and wants Job and CronJob semantics rather than a separate scheduler.

FAQs

Specific to using Batch API API through Jentic.

What authentication does the Batch API use?

The API uses Google OAuth 2.0 with cloud-platform scope. Service accounts must hold the roles/batch.* IAM role and have permission to act as the runner service account used by the job's VMs. Through Jentic the credential lives in the encrypted vault and the agent only sees a short-lived access token.

Can I cancel a Batch job that is no longer needed?

Yes. POST to /v1/{name}:cancel where name is the job resource path. Tasks already running are terminated and the job transitions to a cancelled state.

How do I submit a Batch job through Jentic?

Search Jentic for 'submit a Google Batch job'. Jentic returns POST /v1/{parent}/jobs with its full schema; build a job spec referencing your container image and task count, then execute. The credential is supplied from the Jentic vault.

What are the rate limits for the Batch API?

Numeric limits are not declared in the spec; quotas are enforced per project on batch.googleapis.com along with the underlying Compute Engine quotas the job consumes. Check the project's quotas before scaling up tasks per job.

Where do Batch job results go?

The API does not store task outputs itself. Tasks typically write results to Cloud Storage, BigQuery, or another service of the user's choosing; the job spec defines the runnable that produces those outputs.

Can a running task report custom progress?

Yes. From inside a task, POST to /v1/{parent}/state:report with the task identifier and progress payload. This is useful when stages within a single task need observable progress.