AWS Cost Management

Launch: AWS Cost Explorer Rightsizing Recommendations Integrates with AWS Compute Optimizer

AWS Cost Explorer now delivers rightsizing recommendations across EC2 instance families through an integration with AWS Compute Optimizer, adding to existing support for rightsizing recommendations within the same instance family.

To access Cost Explorer Rightsizing Recommendations, click on “Recommendations” in the left navigation pane, followed by “View All” in the “Resource optimization recommendations” section of the page. You may also access recommendations via the Cost Explorer API.

The master (payer) account can enable Cost Explorer at a root level, allowing all member (linked) accounts to access Cost Explorer.  However, member (linked) accounts need permissions set by the master (payer) account to view their rightsizing recommendations. For more information, see Getting Started with Rightsizing Recommendations.

Filtering Your Recommendations

You can now filter recommendations on the right-hand side of the page by choosing whether to see recommendations within the same instance families or across instance families, adding to existing support for filtration by member (linked) account, region, and tag.

How Are Recommendations Generated?

The recommendations are generated using Compute Optimizer’s machine learning-based recommendation engine. The engine analyzes the configuration and resource utilization of a workload to identify dozens of defining characteristics (e.g. whether a workload is CPU-intensive or if it exhibits a daily pattern). The recommendations engine analyzes these characteristics and identifies the hardware resource headroom required by the workload. Then, it infers how the workload would perform on various Amazon EC2 instances, and makes recommendations for the optimal AWS compute resources for that specific workload.

How Are Savings Associated with Recommendations Calculated?

We first examine the instance running in the last 14 days to identify if it was partially or fully covered by an RI or SP, or if it’s running On-Demand. Another factor is whether the RI is size-flexible. The cost to run the instance is calculated based on the On-Demand hours and the rate of the instance type.

For each recommendation, we calculate the cost to operate a new instance. We assume that a size-flexible RI will cover the new instance in the same way as the previous instance, if the new instance is within the same instance family. Estimated savings are calculated based on the number of On-Demand running hours and the difference in On-Demand rates between the existing instance and the newly recommended instance. If the RI isn’t size-flexible, or if the new instance is in a different instance family, the estimated savings calculation is based on if the new instance had been running during the last 14 days as On-Demand.

Cost Explorer provides only recommendations with estimated savings greater than or equal to $0. These recommendations are consistent with what Compute Optimizer generates. Visit Compute Optimizer for additional performance-based recommendations that may result in a cost increase.

Sharing Your Cost Optimization Insights with Others

From the Cost Explorer Rightsizing Recommendations UI, you can download a recommendation report for all member (linked) accounts within your organization at the master (payer) account level or for specific member (linked) accounts based your custom filter criteria. You can download the recommendation report in a CSV format.

These reports can improve transparency of cost savings across your organization and help foster a cost-aware culture.

To learn more about how to leverage rightsizing recommendations to reduce your costs, see the Rightsizing Recommendations User Guide.

 

Blog authors:

Marianna Tishchenko, Senior Product Manager, AWS Cost Management

Letian Feng, Principal Product Manager, AWS Compute

Bowen Wang

Bowen Wang

Bowen is a Senior Product Marketing Manager for Billing and Cost Management services. She focuses on enabling finance and business leaders to better understand the value of the cloud and ways to optimize their cloud financial management. In her previous career, she helped a tech start up launch their business automation product into the China market and set up a local customer service call center.