AWS Fargate pricing and optimization guide

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 container orchestration services. 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 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.

By following best practices, you can effectively manage your resources on Fargate and avoid overspending. With the right approach for your workload, you can maximize the benefits of this powerful tool.

AWS Well-Architected Framework

The AWS Well-Architected Framework provides extensive guidance on how to design and operate reliable, cost-effective cloud systems. One of the key pillars of this framework is cost optimization, which provides best practices for running workloads on AWS. This pillar is fundamental for infrastructure planning and deciding whether Fargate is a good fit for your deployment.

Cloud computing has different cost drivers, including data transfer, storage, and compute usage. By analyzing these cost factors and using tools like AWS Cost Explorer, you can identify areas where cost reduction is possible without sacrificing performance or reliability.

Another critical aspect of cost optimization is scalability. Fargate allows users to scale their deployments up or down, depending on workload demands, and avoid paying for unused resources.

AWS Fargate pricing

AWS Fargate offers a pricing model 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 arise 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/memory configurations. It’s much cheaper—but only practical if your workloads are interrupt tolerant.

Take this example from the AWS Pricing Calculator:

• 4 tasks per day (121.67 tasks per month)
• 2 vCPUs
• 8 GB memory
• 40 GB ephemeral storage
• 60h 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 8.00 GB 
x 60 hours 
x 0.004445 USD per GB per hour 
= 259.60 USD for GB hours 


  40 GB 
- 20 GB included storage 
= 20.00 GB billable ephemeral storage per task 


  121.67 tasks 
x 20.00 GB 
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/memory configuration changes might yield little savings. Running your containers in the same region as the services they interact with will save 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/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. They offer discounts of up to 66% on computing services, including Fargate, but require a 1–3-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 toselectthe 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 prices. This option is approximately 70% cheaper than standard CPU/memory configurations.

For additional insights into metrics, please read our article on Key Metrics for AWS Fargate Monitoring.

Monitoring and analyzing costs with AWS services

You can’t optimize your costs without knowing what drives them. AWS offers a range of cost monitoring and analysis services that allow you to control expenses and stay within your budget.

AWS Cost Explorer is a tool for tracking usage and cost trends for Fargate services. It provides a comprehensive overview, allowing 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.

AWS Budgets 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. By monitoring your spending across multiple accounts and services with this service, you can gain insight into your usage patterns. This information can then be used to optimize resource allocation and make informed decisions about your IT infrastructure.

AWS Trusted Advisor delivers a range of automated improvement suggestions for your resources. As a key feature, this service offers cost optimization checks to help you identify potential areas of overspending, and makes recommendations for how to cut costs.

Amazon CloudWatch allows you to easily monitor resource usage and configure alarms for scaling actions; in other words, you can set thresholds for resource usage. When those thresholds are exceeded, CloudWatch will automatically scale your resources up or down to ensure you have the right resources available.

With ManageEngine CloudSpend, a third-party service for analyzing workloads, you can group resources in business units with their own budget, scale down services when budgets are exhausted, and estimate monthly usage based on the previous month. CloudSpend provides granular control over your resources to manage Fargate costs more effectively than with AWS services.

Conclusion

AWS Fargate offers an effective and convenient way to handle containerized workloads without the hassle of managing the underlying infrastructure. Nevertheless, 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 AWS Cost Explorer, AWS Budgets, Amazon CloudWatch, and 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.