AWS Fargate is a powerful tool for running containerized workloads on AWS. It’s a serverless compute engine that allows you to run containers and focus on developing and deploying your applications while AWS controls the cloud infrastructure. This can make a real difference for an organization, saving both time and resources that would otherwise go towards managing servers.

This guide will discuss AWS Fargate pricing and provide tips for cost optimization.

AWS Fargate   

Fargate is designed to work with Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS), both fully managed services for container orchestration. Users can run containers on demand without explicitly provisioning and managing servers, which makes scaling applications up or down simpler. It also guarantees that you are only charged for the resources you use.

The importance of cloud cost optimization  

To manage your resources efficiently and avoid unnecessary costs when running containerized workloads on Fargate, understanding the pricing model before purchasing is crucial.

Since the pricing is based on the vCPU and memory resources used by containers and the duration of their execution, running resource-intensive applications for extended periods can quickly get expensive.

Moreover, running multiple containers with varying resource requirements can lead to underutilized resources and wasted spending. As such, regularly monitoring your usage and optimizing your container configurations to reduce costs while maintaining performance is vital.

With the right approach for your workload, you can maximize the benefits of this powerful tool.

AWS Fargate pricing  

Pricing models are based on several factors.

The primary factor is the actual execution duration of the container. It starts at a duration of one minute and increments in one-second steps. The other factors are the resources a container uses (like the number of parallel running tasks on ECS or pods on EKS), the number of vCPUs, memory size, ephemeral storage size, operating system, and CPU architecture.

There are regional differences in the pricing of these resources, and additional costs can be incurred with the usual data transfer fees and when using other AWS services, like AWS CloudWatch Logs.

Fargate Spot offers an alternative to the standard vCPU or memory configurations. It’s much cheaper—but only practical if your workloads are interruption tolerant.

Take this example from the AWS Pricing Calculator:

● 4 tasks per day (121.67 tasks per month)

● 2 vCPUs

● 8GB memory

● 40GB ephemeral storage

● 60 hours per month


  121.67 tasks

x 2 vCPU

x 60 hours

x 0.04048 USD per hour

= 591.02 USD for vCPU hours


  121.67 tasks

x 8GB

x 60 hours

x 0.004445 USD per GB per hour

= 259.60 USD for GB hours



– 20GB included storage

= 20.00GB billable ephemeral storage per task


  121.67 tasks

x 20GB

x 60 hours

x 0.000111 USD per GB per hour

= 16.21 USD for ephemeral storage GB hours


  591.02 USD for vCPU hours

+ 259.60 USD for GB hours

+ 16.21 USD for ephemeral storage GB hours

= 866.83 USD total

Cost optimization strategies  

There are two potential strategies to optimize costs for Fargate: Analyze workload requirements to rightsize your resources accordingly and optimize your tasks and container definitions.

Rightsizing resources  

If you run an I/O-bound workload, CPU or memory configuration changes might yield little savings. Running your containers in the same region as the services they interact with will save you more on data transfer fees.

Suppose you’re running a computation-heavy workload, like executing a large language model. In this case, your best bet is to opt for a CPU or memory configuration that is more expensive per second but has a much shorter execution duration.

Another way to optimize costs is to leverage AWS Savings Plans. These offer discounts of up to 66% on computing services, including Fargate, but require a one-to-three-year commitment of consistent use. It might sound counterintuitive to save money with the constant use of a serverless compute engine like Fargate, but on-demand pricing isn’t the only benefit Fargate brings—it also relieves your team of managing the infrastructure that powers their containers.

Optimizing task and container definitions  

One effective method for cost optimization in AWS is optimizing your task and container definitions. By utilizing the ECS CLI, you can ensure that your task definitions are kept up to date and run as efficiently as possible.

In addition, the AWS Copilot CLI provides a straightforward means to select the appropriate application architecture for your workload.

Another area to scrutinize is data transfer and storage: A close analysis of the data you’re storing and sending over the wire can reveal additional cost-saving opportunities. For example, alternative transport formats or eliminating unnecessary data fields can significantly reduce expenses.

You should also consider compiling your workload’s binaries. If you can compile them to the ARM architecture, you can cut costs up to 20% compared to x86 CPUs.

Furthermore, if your workload is tolerant of interruptions and not time critical, you can take advantage of Fargate Spot’s lower priced capacity. This option is approximately 70% cheaper than standard CPU or memory configurations.

Analyzing costs with CloudSpend   

You can’t optimize costs without knowing what drives them. There’s a range of tools offering cost monitoring and analysis services that can help you stay within your budget.

ManageEngine CloudSpend is a cloud cost management tool for tracking usage and cost trends for Fargate services. Its detailed overview allows you to analyze expenditures and make informed decisions. You can use filters to quickly identify areas to lower expenses and optimize your resource usage. Customized reports and visualizations make it easier to understand the data and share it with teams.

CloudSpend also lets you set a customized cost and usage budget for your AWS resources to prevent unexpected charges from impacting your business. You can define budget limits and receive timely alerts when your usage approaches the threshold or exceeds it.

With Tag Profiles, you can group your resources in the way you deem important. You can use the tags AWS generates for your resources, or use custom tags set by you. For example, you can group resources that are run by Fargate, but split them by a custom project tag you’ve defined for each of your container instances. This way, you can get insights into each of your projects, even if all of them are running in one cluster.

You can also configure CloudSpend to automatically scale your infrastructure to keep your services running longer when they’re close to a spending limit. By using CloudSpend to monitor your spending across multiple accounts and services, you can gain insight into your usage patterns. This information can prove invaluable for optimizing resource allocation and making informed decisions about your IT infrastructure.

Leverage cost optimization in AWS Fargate

AWS Fargate offers an effective and convenient way to handle containerized workloads without the hassle of managing the underlying infrastructure. Still, it is imperative to understand its pricing system and implement cost-saving measures to maximize its advantages and get an optimal return on your investment.

By leveraging tools and services like ManageEngine CloudSpend, you can effectively monitor and analyze costs to make informed decisions about resource allocation and utilization.

Rightsizing resources, optimizing task and container definitions, and considering alternative pricing options like Fargate Spot and AWS Saving Plans can all contribute to significant cost savings. Additionally, adhering to the AWS Well-Architected Framework and focusing on cost optimization as a key pillar can help guide your infrastructure planning and resource management decisions.

By incorporating these strategies and regularly monitoring your usage, you can maintain high performance while keeping costs in check, allowing you to focus on building and deploying successful applications with AWS Fargate.