5 ways to forecast your cloud spend

5 ways to forecast your cloud spend

Why do you need to forecast?

In every company there is a need to plan for future flows of money, and IT is no exception. Before the Cloud, IT spend was driven by people costs, hardware costs, and maintenance costs, each of which is stable under change management (i.e. you need authorisations to change things).

Then, one day cloud is used and the costs are less predictable. What was a server, with a buying and maintenance cost, becomes a dynamic set of virtual machines, with different network, storage, size that can change on an hourly basis. While resulting in better price and performance, the cloud complicates forecasting a lot. There is no one-size-fits-all approach and so IT departments are left to become creative.

In this article I will investigate five cloud spend forecast methods with their pros and cons to help you.

Disclaimer: Unless you are in a very boring business where nothing changes a forecast will always be wrong! Business life will happen, and no model can account for every aspect of reality.

1. My usage is constant and planned to stay constant

This approach is useful when the forecast timeframe is short (e.g. one week) or when IT is not involved in the operational aspects of the business. For example, when IT only provides internal services like email and a website, and not used to deliver to customers. Another example is automated manufacturing, where IT is the brain of the machinery and unless new machines are bought (or removed) nothing will change. -Summary: Take the last period consumption and assume the same for the future.

Pro Con
Immediately available Extremely sensitive to change
No effort needed
Very accurate when nothing changes

2. Take the last period and add/remove X%

Many companies have asked me for help with their yearly cloud forecast. When I’ve asked them how they were doing today, the answer was we add 20% to last year’s spend. I guess this is part of a ritual, where each department asks for more than last year and finance provides a subset of the request each year.

Pro Con
Very fast Very generic
Very easy Very static number (when the number is not reviewed often)
Backed by business data (kind of)

3. Using AWS Cost Explorer Forecast (or other cloud vendor tools)

When accessing the AWS cost explorer, one of the first pieces of information presented is a forecast for next year, based on last year data and elaborated by a huge-sized company. The graph is very good-looking, and is easy to add into a document, but I would not recommend you to use it to make decisions.

Pro Con
Available immediately Based only on data available to the cloud vendor, so no business connection
An innovative company offers the results Future scenario modelling requires the use of additional tools (e.g. Excel)
The graphs look great The methodology is unknown, therefore difficult to justify
Provides minimum, maximum and expected values The accuracy drops fast

4. End-of-month forecast

Like in a car, sometimes what you need is the current speed and an idea of where that speed will get you over the next few hours. For cloud spend, the equivalent is the end-of-month forecast.

Pro Con
Available in the Strategic Blue dashboard Needs to be implemented
Great to show the impact of cloud initiatives Data management and ETL knowledge is a requirement
Capabilities to follow the monthly budget on a day-to-day basis Understanding the cloud vendor’s financial reporting files is a must (e.g. AWS Cost and Usage Report)

5. Use business data (sales projections)

When there is a culture of sales forecast in the company, IT should leverage the good work of other groups and identify a connection between the business element of income (e.g. customers or transactions) and cloud costs. For example, identifying that each transaction costs 1 cent allows you to use the sales forecast to build the cloud cost one.

Pro Con
Converts IT slang into money, making it easy to understand for other departments Complex to implement
Easy to recalculate, even daily Requires bespoke modelling
By mapping cloud costs to the company units of goods sold, the results are more comprehensible to non-IT A change in the business model requires an update in the algorithm
Any IT effort to reduce the cost per customer is made visible
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