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AWS Batch Reviews

Vendor: Amazon Web Services (AWS)
4.2 out of 5
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What is AWS Batch?

AWS Batch enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. AWS Batch dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted. With AWS Batch, there is no need to install and manage batch computing software or server clusters that you use to run your jobs, allowing you to focus on analyzing results and solving problems. AWS Batch plans, schedules, and executes your batch computing workloads across the full range of AWS compute services and features, such as Amazon EC2 and Spot Instances.

Get the AWS Batch Buyer's Guide and find out what your peers are saying about AWS Batch, Apache Spark, AWS Lambda and more!
AWS Batch is the #6 ranked solution in top Compute Service solutions. PeerSpot users give AWS Batch an average rating of 8.4 out of 10. AWS Batch is most commonly compared to Apache Spark: AWS Batch vs Apache Spark. AWS Batch is popular among the large enterprise segment, accounting for 75% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a financial services firm, accounting for 31% of all views.
Buyer's Guide
AWS Batch
December 2025
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AWS Batch mindshare

As of December 2025, the mindshare of AWS Batch in the Compute Service category stands at 14.1%, down from 19.1% compared to the previous year, according to calculations based on PeerSpot user engagement data.
Compute Service Market Share Distribution
ProductMarket Share (%)
AWS Batch14.1%
AWS Lambda15.2%
Apache Spark11.4%
Other59.300000000000004%
Compute Service

PeerResearch reports based on AWS Batch reviews

TypeTitleDate
CategoryCompute ServiceDec 30, 2025Download
ProductReviews, tips, and advice from real usersDec 30, 2025Download
ComparisonAWS Batch vs AWS LambdaDec 30, 2025Download
ComparisonAWS Batch vs AWS FargateDec 30, 2025Download
ComparisonAWS Batch vs Apache SparkDec 30, 2025Download
Suggested products
TitleRatingMindshareRecommending
Apache Spark4.211.4%90%68 interviewsAdd to research
AWS Lambda4.315.2%94%90 interviewsAdd to research
 
 
Key learnings from peers

Valuable Features

AWS Batch excels in scalability and parallelism, handling workloads from small to extensive. Users value the flexibility to customize compute and memory requirements. It integrates seamlessly with EC2, S3, Lambda, Step Functions, and supports Docker containers. Templates streamline configuration, while security features like IAM roles ensure controlled access. It's praised for cost-effectiveness, especially with Fargate, enhancing efficiency by eliminating dependencies on EC2 instances, and offering massive scaling with minimal setup.
  • "AWS Batch is a cost-effective way to perform batch processing, primarily using spot instances and containers."
  • "AWS Batch is highly flexible; it allows users to plan, schedule, and compute on containerized workloads, create clusters tailored to specific needs like memory-centric or CPU-centric workloads, and supports scaling operations massively, like running one hundred thousand Docker containers simultaneously."
  • "I appreciate that AWS Batch works with EC2, allowing me to launch jobs and automatically spin up the EC2 instance to run them; when the jobs are completed, the EC2 instance shuts down, making it cost-effective."

Room for Improvement

AWS Batch faces challenges with cost-effectiveness, documentation, UI glitches, and integration with other AWS services, causing difficulties for junior developers. Improved pricing, error handling for Spot Instances, cold start issues, and Fargate startup times are needed. Users suggest enhancements in deployment, logging, job monitoring, and IAM privilege setup. Faster log displays, advanced error handling, and better GUI descriptions would benefit technical and non-technical users. Scalability, reliability, and dynamic resource allocation require further development.
  • "The solution should include better and seamless integration with other AWS services, like Amazon S3 data storage and EC2 compute resources."
  • "When we run a lot of batch jobs, the UI must show the history."
  • "The main drawback to using AWS Batch would be the cost. It will be more expensive in some cases than using an HPC. It's more amenable to cases where you have spot requirements."

Pricing

AWS Batch users find the pricing to be fair and cost-effective, especially when utilizing spot instances for tasks. While AWS Batch itself incurs no direct cost, users pay for underlying resources like AWS EC2 and AWS Fargate. Costs, such as the $53 monthly expenditure reported, are considered justified and manageable. Users appreciate the flexibility in controlling costs, highlighting AWS Batch as a budget-friendly option for managing high volumes of tasks efficiently.
  • "AWS Batch is a cheap solution."
  • "The pricing is very fair."
  • "AWS Batch's pricing is good."

Popular Use Cases

AWS Batch is used for running computational jobs by breaking them into smaller tasks across multiple machines. Users leverage its flexibility for scalable, containerized workloads, dynamic scaling, and seamless integration with other AWS services. It facilitates data processing, high-frequency data extraction, secure code execution, and managing complex analysis pipelines. Organizations use it for simulations, on-demand computations, and data backups. Its versatility supports applications in data science, analytics, and large-scale energy data management.

Service and Support

AWS Batch customer service and support offer prompt responses based on selected support tiers. Additional payment enhances response times, with options for immediate assistance during critical needs. Support teams are praised for their knowledge and efficiency, often responding faster compared to Azure. Ticketing systems and chat support provide rapid communication, whereas phone support is slower. While overall quality is satisfactory, improvements in logging information accessibility are suggested.

Deployment

AWS Batch's initial setup varies based on experience. Those with an appropriate skill set find it easy and quick, often taking between 15 minutes to an hour. The process can be more straightforward compared to Azure Batch. Available AWS documentation and blogs aid the process, though some find the official documentation complex, requiring alternative resources. Users generally consider it accessible through the AWS console, with an understanding curve for new users.

Scalability

AWS Batch offers strong scalability, supporting varied computational demands. Users express satisfaction, highlighting robust horizontal scaling capabilities, accommodating increased job concurrency. Adjustments in CPU and RAM efficiently manage large-scale applications. It can handle numerous concurrent processes, adept for adjusting to workload changes and enhanced virtual CPU quotas. Users rely on AWS Batch’s reliance on services like AWS Fargate and EC2, acknowledging the importance of instance selection. Scalability is consistently rated highly with no noted issues.

Stability

AWS Batch remains stable for many users, scoring high ratings in terms of dependability. Instances of outages exist, usually linked to specific configurations or AWS service interruptions, although redundancy mitigates these risks, ensuring consistent uptime. Users experience occasional issues, such as job termination difficulties and job duplications, but thousands of jobs run without significant problems. With sufficient resource allocation, users find AWS Batch flexible and robust.
These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.
Download our AWS Batch Buyer's Guide for additional reliable information.

Review data by company size

By reviewers
Company SizeCount
Small Business5
Large Enterprise4
By reviewers
By visitors reading reviews
Company SizeCount
Small Business33
Midsize Enterprise12
Large Enterprise137
By visitors reading reviews

Top industries

By visitors reading reviews
Financial Services Firm
31%
Manufacturing Company
9%
Computer Software Company
7%
Comms Service Provider
6%
University
5%
Healthcare Company
4%
Retailer
4%
Government
3%
Insurance Company
3%
Media Company
3%
Wholesaler/Distributor
2%
Construction Company
2%
Consumer Goods Company
2%
Outsourcing Company
2%
Recreational Facilities/Services Company
2%
Real Estate/Law Firm
2%
Energy/Utilities Company
2%
Educational Organization
2%
Marketing Services Firm
1%
Pharma/Biotech Company
1%
Legal Firm
1%
Hospitality Company
1%
Renewables & Environment Company
1%
Non Profit
1%
Performing Arts
1%
Local Government
1%
Transportation Company
1%
Leisure / Travel Company
1%
Logistics Company
1%

Compare AWS Batch with alternative products

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Learn more about AWS Batch

AWS Batch was previously known as Amazon Batch.

AWS Batch customers

Hess, Expedia, Kelloggs, Philips, HyperTrack

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Product Categories

Product Categories
Compute Service

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AWS Batch Reviews Summary
Author infoRatingReview Summary
Software Engineering Manager – Digital Production Optimization at Yara International ASA4.0No summary available
Senior Battery Data Engineer at a agriculture with 51-200 employees4.5No summary available
Head of Development at Abyss3.5No summary available
Senior Data Engineer at a tech services company with 5,001-10,000 employees4.5No summary available
Independent Consultant at a consultancy with 1-10 employees4.0No summary available
Works3.5No summary available
Head of Bioinformatics at Paratus Sciences4.5AWS Batch offers flexibility similar to HPC environments, allowing scalable compute resources without significant hardware investment. While more costly than some HPC setups, its quick setup and adaptability suit projects with varying resource needs, making cloud deployments efficient and effective.
DevOps Engineer at ZoomOps Technology5.0We use AWS Batch to manage containerized workloads with dynamic scaling and integration with AWS services. While it efficiently handles job scheduling, provisioning, and scaling, improvements are needed in pricing, integration, scalability, reliability, and keeping information up-to-date.