AWS Fargate

Serverless compute for containers

Deploy and manage your applications, not infrastructure. Remove the operational overhead to scale, patch, help secure, and manage servers.

Monitor your applications through built-in integrations with AWS services like Amazon CloudWatch Container Insights or gather metrics and logs with third-party tools. 

Improve security through workload isolation by design. Amazon ECS tasks and Amazon EKS pods run in their own dedicated runtime environment.

Pay only for compute resources used, with no upfront expenses. Further optimize costs with Savings Plans, Fargate Spot, or AWS Graviton processors. 

How it works

AWS Fargate is a serverless, pay-as-you-go compute engine that lets you focus on building applications without managing servers. AWS Fargate is compatible with both Amazon Elastic Container Service (Amazon ECS) and Amazon Elastic Kubernetes Service (Amazon EKS). Select any OCI-compliant container image, define memory and compute resources, and run the container with serverless compute. With multiple CPU architectures and operating systems supported, you can enjoy the benefits across a wide variety of applications.

Diagram shows the benefits gained by using AWS Fargate to run containerized applications.
Introduction to AWS Fargate (0:46)
Introduction to AWS Fargate
Watch this introductory video that explains what AWS Fargate is in under a minute. AWS Fargate is a serverless compute engine compatible with Amazon ECS or Amazon EKS that allows you to run containers without having to manage servers or clusters.

Use cases

Web apps, APIs, and microservices

Build and deploy your applications, APIs, and microservices architectures with the speed and immutability of containers. Remove the need to own, run, and manage the lifecycle of a compute infrastructure, so you can focus on your applications.

Modernize applications

Use AWS Fargate with Amazon ECS or Amazon EKS to more easily run and scale your containerized workloads. Migrate and run your Amazon ECS Windows containers without refactoring or rearchitecting your legacy applications.

Support AI and ML applications

Create a flexible and portable artificial intelligence and machine learning (AI/ML) development environment. Train, test, and deploy your ML models with scalable resources that boost server capacity while avoiding overprovisioning.

Data processing

Run data processing workloads, scale up to 16 vCPU and 120 GB memory per task, and integrate with AWS Batch for serverless parallel processing.

How to get started

Learn more about AWS container services

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