During the pandemic, Companies have made a major shift toward cloud investments.. Until a few years ago, all internal applications, including finance-related applications, were hosted on on-premise servers. These on-prem servers have fixed capacity and cost. Any changes to these On-prem server configurations required buy-in from the tech team as well as the finance teams and CFO. However, since the introduction of cloud computing, the cloud cost has become a variable of the processing/data load. The finance teams’ visibility of cloud costs and their prediction has dramatically decreased. Finance teams have been struggling to adjust to this new normal. This is attributable to a number of causes, including
- Lack of accountability
- Lack of controls
- Finance teams traditional processes of forecasting
Engineering teams can enable accountability and control by using various cloud-ops tools. These tools bring standardization and governance to cloud capacity building. Finance teams should be made part of the this governance and approval process.
Cloud costs forecasting for P&L:
The usual technique for any OpEx forecasting by finance teams involves bottom-up calculations. The dynamic nature of cloud cost variations does not lend itself to this strategy. The cost of cloud computing is determined by a variety of factors, including server load, new projects, the number of users, and data storage etc. To forecast accurately, all of these aspects must be taken into account.
Bringing all of the above-mentioned data points together and modeling them in Excel is a massive undertaking in itself. This is where financial teams may benefit from machine learning. Machine learning can take into account previous costs as well as factors such as server loads, new projects, and so on.
Self service finance AI tools like Tadaa.ai has an inbuilt workflow for predicting cloud costs which helps finance teams by reducing the time and effort taken during every month end. It also aids in the identification of the many unknown drivers that influence the unpredictability of cloud costs by including various data points and measuring the accuracy.
