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AWS Batch vs AWS Fargate: What are the differences?

AWS Batch and AWS Fargate are both container-based solutions on AWS. Let's explore the key differences between them:

  1. Deployment Model: AWS Batch is a fully managed service that lets you run batch computing workloads while AWS Fargate is a serverless compute engine for containers. With AWS Batch, you specify the compute resources needed for your batch jobs, while with AWS Fargate, you just need to define your containers and let AWS take care of managing the underlying infrastructure for you.

  2. Scalability: AWS Batch enables you to automatically scale your compute resources based on the needs of your batch jobs. It provides dynamic scaling and takes care of efficiently distributing workloads. On the other hand, AWS Fargate automatically scales your containers based on the resource requirements specified in your task definitions. It allows you to run multiple container instances across multiple Availability Zones to handle increased traffic or workload.

  3. Resource Management: AWS Batch allows you to manage your compute resources by creating compute environments and managing job queues. You have more control over the allocation and utilization of resources. AWS Fargate, on the other hand, abstracts away the need for managing compute resources. You simply define your containers and let AWS Fargate handle the allocation and scaling for you.

  4. Cost Structure: AWS Batch offers a pricing model based on the compute resources used and the duration of the job. You have more control over the costs by choosing the type and size of instances to use. AWS Fargate, however, operates on a pay-as-you-go model, where you only pay for the resources consumed by your containers. It is more cost-effective for short-lived and variable workloads.

  5. Integration with other AWS Services: AWS Batch integrates seamlessly with other AWS services like Auto Scaling, Elastic Load Balancing, and CloudWatch. You can easily configure these services to enhance the scalability and monitoring of your batch jobs. AWS Fargate also integrates with various AWS services, but it provides a more simplified and managed experience, allowing you to focus more on your application development rather than infrastructure management.

  6. Container Management: AWS Batch provides extensive support for container management by supporting various containerization technologies like Docker. You can easily package your batch jobs in containers and manage them using AWS Batch. AWS Fargate, on the other hand, is specifically designed for managing and running containers, providing a more streamlined and simplified experience.

In summary, AWS Batch is designed for batch processing workloads, offering control over compute resources and job scheduling, suitable for scenarios like data processing pipelines. AWS Fargate, on the other hand, abstracts away infrastructure management, making it ideal for running individual containers without the need to manage clusters, suitable for microservices and containerized applications with a serverless approach.

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Pros of AWS Batch
Pros of AWS Fargate
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    Containerized
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    Scalable
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    Cons of AWS Batch
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      More overhead than lambda
    • 1
      Image management
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      Expensive

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    What is AWS Batch?

    It enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. It 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.

    What is AWS Fargate?

    AWS Fargate is a technology for Amazon ECS and EKS* that allows you to run containers without having to manage servers or clusters. With AWS Fargate, you no longer have to provision, configure, and scale clusters of virtual machines to run containers.

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    What are some alternatives to AWS Batch and AWS Fargate?
    AWS Lambda
    AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.
    Beanstalk
    A single process to commit code, review with the team, and deploy the final result to your customers.
    Airflow
    Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
    Kubernetes
    Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions.
    JavaScript
    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles.
    See all alternatives