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Introduction to AWS Fargate

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  • Last Updated : 28 Mar, 2023
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AWS Fargate is a feature in container services in Amazon Web Services which can be used to run your containers without having to manage the server or the underlying architecture. It is a serverless computing engine for containers.  It is a pay-as-you-go model, i.e. pay for the resources you are using.

Furthermore, it can be used with container services in AWS like ECS (Amazon Elastic Container Service) and EKS (Amazon Elastic Kubernetes Service). It is one of the most important features of serverless computing in AWS. Whenever you opt for Fargate, the infrastructure will be owned and managed by AWS. It saves you time by automating features like patching, updating, and dealing with resources.

The operating systems supported by Fargate are Amazon Linux 2, Windows server 2019 Full, and Windows server 2019 core.

Components of Fargate:

1. Clusters – Clusters are the logical grouping of the tasks and services in Fargate.
2. Task Definitions – Task Definitions are text files where the user can describe one or more containers that form the applications. They are used to define various specifications or parameters to be used in Fargate containers. They can be called the blueprint of your Fargate application.
3. Tasks – A task is the initialization or instantiation of the task definition. Users can create multiple tasks from the same task definition in a cluster. Users can run a single task or multiple tasks at a time.
4. Services – Services is a tool in Fargate to run the tasks that have been created. This can be used to run multiple tasks at a time in a cluster. They also replace the tasks with new tasks based on task definition if the existing task fails or stops.

Working of Fargate:

To utilize container service using Fargate following steps need to be followed:

  1. Build a container image
  2. Use services like Amazon ECR or Dockerhub to host it.
  3. Use Amazon EKS or ECS for the execution of the same.
  4. Finally, create a cluster using Fargate service.

Fargate and EC2:

  • You are responsible for the underlying OS in EC2, while in Fargate you have no OS access. In this, Fargate is beneficial if you want to just focus on running the application rather than managing it.
  • In Fargate you pay for the resources allocated and time for which it ran, while in EC2 you have different pricing models. In the case of EC2, you can use different pricing models like Spot instances or Reserved and manage your costs for application according to that.
  • EC2 consists of long-running containers, while Fargatethe are short-running tasks. 

Use Cases of Fargate and EC2:

  •  Large workload, low cost: If the a user has requirement for large workloads of memory and CPUs and the user wants comparatively low prices, then EC2 is preferred. In EC2, user has the advantage of choosing different pricing models like spot instances.
  • Large the , less manual effort: If the user has requirement for large workloads of memory and needs to put in as less manual effort as possible, then Fargate is preferred. In EC2 all instances need to be monitored by the user using it and the management of the same needs to be done, while in Fargate these parameters are handled by AWS.
  • Small workload, with occasional spikes in workload: If a  user has a requirement for small workloads but occasional bursts of memory at a particular time, then Fargate is preferred. Fargate helps in saving costs for such occasional bursts.
  • Very small workloads: For tiny workloads like small test environments, Fargate is preferred as EC2 are more complex and powerful for such kind of tasks.
  • Workloads in Batch: If the user has requirement for workloads that are in batches like time-based or occasional jobs, then Fargate is preferred. For such tasks, if user uses EC2 then they have to manually keep on starting and stopping instances based on the time schedule of these batch jobs to save costs.
  • Fargate can be used to create and deploy microservices architectures, APIs, and web applications. Containerized data can be run on it and scaled. Fargate enables AI the  ML applications, and it aids in scaling up the development, deployment, and testing of your ML models. Fargate reduces upfront expenses because the user only pays for the resources they really utilize.

Fargate and Lambda:

Fargate can be used for more consistent and predictable workloads, while Lambda can be used for unpredictable and inconsistent workloads.


1. Lesser Cost: For complex tasks, AWS Fargate is a cheaper option than Amazon ECS or EKS.
2. Security: Since users don’t have much control over each container in the system and most of the maintenance and security are handled by AWS, security concerns are less.
3. Less work: Users don’t have to be concerned about handling every detail like scaling of containers in Fargate, so there is less effort involved for the user.
4. Less Complexity: It does away with the requirement for server administration. It is therefore a “serverless” technology. You no longer need to bother about customizing and managing the servers; your containers will still run on them.


1. Not recommended for simple tasks: For simple tasks, AWS Fargate might be a costly option as compared to Amazon ECS or EKS.
2. Less control for users: For users who have requirements to customize each and every detail, Fargate is not a good option, as it does not give users a lot of control.
3. Availability: Less available regions as compared to Amazon ECS or EKS.


Fargate is a really powerful and helpful service provided by AWS for container services. It helps users to save a lot of time, effort, and cost while using Container services. If a user is new to AWS and the concept of containers in AWS, then this is the go-to tool as it helps in focusing on just building the application rather than managing it.

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