Over the course of my time here at AWS, I have yet to meet two companies who structure their financial processes in the same way. For this reason, AWS provides several cost datasets tailored to help you better understand your AWS usage, allowing you to account for the unique way that you run your business.
In this post, I describe each of the cost datasets available with AWS Billing and Cost Management and provide a set of best practices to help you decide which cost dataset is right for you.
The vast majority of AWS customers use the unblended cost dataset to understand their usage. This is the cost dataset presented to you on the Bills page. It’s the default option for analyzing costs using AWS Cost Explorer or setting custom budgets using AWS Budgets.
Unblended costs represent your usage costs on the day they are charged to you. In finance terms, they represent your costs on a cash basis of accounting. For most of you, this is the only cost dataset that you will ever need.
In AWS Cost Explorer and AWS Budgets, you see these options presented on the Show costs as menu, as shown in the following screenshot.
These dataset options are also available in the AWS Cost & Usage Report.
Viewing your amortized costs is useful in cases in which it doesn’t make sense to view your costs on the day that they were charged. Or, as many of finance owners say, it’s useful to view costs on an accrual basis rather than a cash basis. This cost dataset is especially useful for those of you who have purchased AWS Reservations such as Amazon EC2 Reserved Instances.
The following diagrams show a quick example of daily EC2 costs over the past few months, when viewed through the lens of unblended vs. amortized costs:
Savings Plans and Reservations often have upfront or recurring monthly fees associated with them. As you can see in the first chart, these recurring fees are charged on the first day of the month. That can lead to a spike on one day, if you are using unblended costs as your cost dataset. When you toggle over to amortized costs, these recurring costs (as well as any upfront costs) are distributed evenly across the month.
Amortized costs are a powerful tool if you seek to gain insight into the effective daily costs associated with your reservation portfolio or are looking for an easy way to normalize cost and usage information when operating at scale.
Blended costs were originally created to support customers who chose to consolidate their billing under a single paying account. Nowadays, these costs are not used frequently due to the way that they are calculated.
Blended costs are calculated by multiplying each account’s service usage against something called a blended rate. A blended rate is the average rate of on-demand usage, as well as Savings Plans- and reservation-related usage, that is consumed by member accounts in an organization for a particular service. Sound complicated? Well, it can get more nuanced, such as in some service-specific examples.
So, unless you have some specific use cases for forecasting based on average rates or something along those lines, I would generally steer away from using the blended cost dataset.
Other cost datasets
In rare cases, customers operating at scale on AWS may be able to take advantage of specialized discounts. The net unblended cost dataset reflects usage costs after these discounts are applied while the net amortized cost dataset adds additional logic to amortize discount-related information, in addition to your Savings Plans or Reservation-related charges.
So, which cost dataset should you use?
If you are a customer who has purchased Savings Plans or Reservations, then amortized costs are most likely the right dataset for analyzing your cost trends. Otherwise, looking at unblended costs should suit you just fine.
Finally, if you are operating at scale or have highly specialized use cases, it may make sense to investigate using the net unblended, net amortized, or blended cost datasets.
To learn more about understanding your organization’s consolidated costs, see Understanding Consolidated Bills.
Reference: AWS Blogs