We performed a comparison between AWS Batch and AWS Fargate based on real PeerSpot user reviews.
Find out in this report how the two Compute Service solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
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.
A new compute engine that enables you to use containers as a fundamental compute primitive without having to manage the underlying instances. With Fargate, you don’t need to provision, configure, or scale virtual machines in your clusters to run containers. Fargate can be used with Amazon ECS today, with plans to support Amazon Elastic Container Service for Kubernetes (Amazon EKS) in the future.
Fargate has flexible configuration options so you can closely match your application needs and granular, per-second billing.
AWS Batch is ranked 6th in Compute Service while AWS Fargate is ranked 8th in Compute Service with 3 reviews. AWS Batch is rated 0.0, while AWS Fargate is rated 8.6. On the other hand, the top reviewer of AWS Fargate writes "A serverless, pay-as-you-go compute engine that you can deploy quickly". AWS Batch is most compared with AWS Lambda, Apache Spark, Oracle Compute Cloud Service and Amazon EC2 Auto Scaling, whereas AWS Fargate is most compared with Amazon EC2 Auto Scaling, Amazon EC2, AWS Lambda, Apache Spark and Apache NiFi.
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