Job queues & batch processing

An infinitely scalable job queue and a batch job system that can provision thousands of containers on a schedule, all on Modal.

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Better than a job queue

Spawn tasks from your backend and poll for the results later. No need to manage Celery or your own queue.

Cron jobs

Modal functions can be run on a schedule with a single line of code. Configure retries, timeouts and back-off in the same place.

def perform_heavy_computation():
Parallel computation

Distribute your work over thousands of containers with a call to .map(). Monitor and debug your runs from the unified web interface.


Modal allows you to chain together functions that have disparate hardware requirements and image definitions. Go from running a GPU endpoint to an entire pipeline with pre- and post-processing that runs on CPU-only containers.

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Sahaj Garg
Sahaj Garg

Modal has made it incredibly easy for a team of researchers and scientists to deploy data science and data processing pipelines on cloud workloads. It means that it’s possible for an R&D team to own the software that scientists are developing, as opposed to handing it off to a team of engineers working on infrastructure.

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