Choosing an AWS Batch alternative#

2025-08-05

6 min read

AWS Batch is powerful, but not always the right fit.

AWS Batch is great when you need a fully managed job orchestration system for running containerized workloads at scale. It shines in production environments where DevOps teams are already managing infrastructure, and where strict job queueing are critical to large-scale batch pipelines.

But, for many data practitioners, it’s overkill. Many people come across AWS Batch when they’ve hit the limits of AWS Lambda and quickly find it’s not nearly as user-friendly. Others are looking for an easy way to run their script on the cloud, but then got stuck configuring job queues and troubleshooting opaque errors in CloudWatch. Then, once you’ve figured out the error, it can take several minutes to rebuild Docker images and try again.

In this post, we’ll cover some of the challenges of AWS Batch and provide some alternatives.

What makes AWS Batch challenging?#

AWS Batch is powerful, but difficult to use for a number of reasons, including:

Complex setup: You need job definitions, compute environments, and container specs, even for simple jobs.
Opaque Debugging: When jobs fail, logs are spread across CloudWatch with minimal helpful context.
Hard to use Spot: AWS Batch can get expensive quickly, and to save on costs Amazon recommends using EC2 Spot. This requires additional management to do well, though, including picking the right allocation strategy, configuring subnets for multiple availability zones, making sure your job isn’t running for too long, and ensuring you have automated retries set up.
Quota limits: One of the more common job stuck in RUNNABLE status culprits is if you don’t have the right quota for the resources you want to use (this could be GPUs or Spot or even an instance type with more memory).

In the sections below we’ll go into getting started with AWS Batch on Fargate and on EC2.

AWS Batch with Fargate#

You’ll first need to decide whether to use AWS Batch with Fargate or EC2. Amazon claims Fargate is their easier, serverless option. There is still a lot of setup required, though, including:

Find default VPC, subnets
Identify security groups
Create required IAM roles
Create a Fargate compute environment with public subnets
Create a job queue linked to your compute environment
Register a job definition

All before you can even submit your job. Once you’re ready to actually submit your job, you can’t actually submit a script. Instead, you’ll need to do one of the following:

Put your script as a JSON argument to Batch (this gets pretty awkward if your script is more than a couple lines)
Build it into the Docker image (OK for production, but slow for development)
Upload your script to S3 (and create and attach the correct IAM roles and access)

Which is a lot of overhead, especially if all you want is to run a script on the cloud. After all that, you also can’t use Fargate with GPUs, so you might instead try EC2.

AWS Batch with EC2 for GPU access#

If you want to use AWS for GPUs, you’ll need to use EC2. That means you’ll first have to go through the EC2 setup, which includes:

Finding (or creating) your VPC, with subnets and security groups
Using (or creating) VPC infrastructure (you may need to create this if you want to use a different region from the default)
Creating the EC2 compute environment with your GPU instances

Then, you’ll be able to start setting up AWS Batch:

Create Batch EC2 compute environment with GPU instances
Create GPU job queue
Register GPU job definition (with your image, GPU resource requirement, and any additional memory your job will need)

After that, you’ll be able to try to run your script.

What to look for in an AWS Batch alternative?#

All of that setup can be a lot of overhead if all you need to do is run a script on the cloud. AWS Fargate and AWS Lambda are both easier to use compared to AWS Batch, but come with their own tradeoffs like not being able to use GPUs and container image size limits.

Feature	AWS Batch	AWS Fargate	AWS Lambda	Coiled
Max execution time	No limit	No limit	15 minutes	No limit
Setup complexity	High	Medium	Low	Low
Container image size limit	No limit (w/ EC2)	20GB (200GB w/ extra config)	10GB	No limit
Containerization required	Yes	Yes	No	No (package sync or Docker)
Built-in parallelism	✅ Yes	❌ No	❌ No	✅ Yes (Dask, Coiled Batch, Coiled Functions)
Interactive development	❌ No	❌ No	❌ No	✅ Yes
Spot instances	✅ Yes (requires extra config)	✅ Yes	❌ No	✅ Yes
GPU support	✅ Yes (w/ EC2)	❌ No	❌ No	✅ Yes
Scale-to-zero	✅ Yes	❌ No	✅ Yes	✅ Yes

Coiled is an easier alternative to these AWS tools, making it easy to go from your laptop to the cloud.

Coiled as an AWS Batch alternative: An easy way to scale scripts on the cloud#

Coiled is an easier alternative to running your workflows on the cloud. It automates the provisioning of EC2 instances in your AWS account, automatically synchronizes your Python packages, and turns things off when you’re done. It’s easy to go from testing interactively in a notebook to scaling out to cluster of VMs, without needing to build Docker containers or manage Kubernetes. You can easily take advantage of heavily discounted Spot instances (most computations end up costing ~$0.10 / TB).

It makes everything so much easier. We can always use the fastest machines. We don’t have to worry about infrastructure. In production we use Docker, but for the researchers we let them just package sync away and not worry about it. It’s really easy to parallelize things that might have previously taken two or three days.

Nelson Griffiths, Head of Engineering at Double River Investments

There are a few ways to use Coiled, but Coiled Batch is the most natural AWS Batch alternative.

Coiled Batch for single jobs to large-scale parallelism#

Coiled Batch is an easy way to run any script on the cloud and is especially useful for scaling large, independent tasks (ie, embarrassingly parallel workloads) across hundreds of VMs. Unlike other Coiled APIs, it doesn’t rely on Dask for parallelism, which means you can run any code (not just Python).

You can use Coiled Batch to:

Run an arbitrary bash script 100 times in parallel
Reproject thousands of satellite images with GDAL for $1
Train a PyTorch model on a GPU for $0.12
Stress-testing S3 concurrency
Multi-node GPU LLM fine-tuning

To run a batch job, add #COILED comments to your script to specify the cloud resources you want:

Bash Script

Spin up ten cloud VMs with 32 GB of memory to run their own echo command.

my_script.sh#

#!/bin/bash

#COILED ntasks 10
#COILED memory 32GB
#COILED container ubuntu:latest

echo Hello from $COILED_BATCH_TASK_ID

Then launch your script with coiled batch run:

$ coiled batch run my_script.sh

COILED_BATCH_TASK_ID is an identifier unique to each task which runs from “0”, “1”, “2”, …, “9” in this case.

Python Script

Use #COILED comments directly in Python scripts. Drop the container directive to use package sync to automatically replicate your Python environment on remote VMs.

my_script.py#

#COILED n-tasks     10
#COILED memory      8 GiB
#COILED region      us-east-2

import os

print(f"Hello from {os.environ['COILED_BATCH_TASK_ID']}")

and then launch your script with coiled batch run:

$ coiled batch run my_script.py

Under the hood Coiled will:

Inspect your script
Spin up appropriate machines as defined by #COILED comments
Download your software onto them or use the container you specified
Run your script
Shut down the machines

Next steps#

It’s easy to get started with Coiled:

$ pip install coiled
$ coiled quickstart

Learn more: