As cloud computing continues to grow in popularity, so does the need for accurate cloud cost forecasting. With cloud costs being variable and unpredictable, it can be difficult to know how much you’re going to spend each month, a problem that can lead to budget overruns and financial problems. This makes cloud cost forecasting a necessary step in owning and managing your cloud efficiently.
In simple terms, cloud cost forecasting is the process of predicting future cloud costs. By forecasting your cloud costs, you can gain visibility into your spending and make informed decisions about how to optimize your cloud resources.
Cost forecasting can be beneficial in many ways:
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Avoid budget overruns – By knowing how much you’re going to spend in the future, you can ensure that you have enough money allocated to your cloud budget.
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Reduce cost – You can see where you’re overspending and take steps to reduce your costs.
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Better business decisions – By knowing how much you’re going to spend in the future, you can make sure that you’re using your cloud resources efficiently.
You can use several methods to forecast your cloud costs. One popular choice is to use historical data to predict future trends; another is to use machine learning (ML) to predict future costs. We combine the best of these two worlds and bring to you: machine learning–powered forecasting.
Introducing machine learning–powered forecasting in VMware Tanzu CloudHealth
We are excited to announce the general availability of machine learning–powered forecasting for Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)! As a user, you can choose up to the last 12 months of historical data to forecast costs for the current month and project up to 36 months in the future. Tanzu CloudHealth factors in seasonality, periodicity, historical trends, and deviations. You also get the ability to see and compare budgets, forecasts, and actuals to understand your portfolio even better.
Features of ML-powered forecasting in Tanzu CloudHealth include the following:
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Customizable forecasting per business requirements
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Ability to visualize past and estimated usage trends in one place with a budget threshold
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Perspective support, allowing forecasting for Perspective groups
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Budget vs. actual vs. forecast depictions enable a better understanding of your portfolio
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Snapshot and compare; save your forecasting reports and compare to other variables
Machine learning–powered forecasting in Tanzu CloudHealth
You also get the ability to feed in your business knowledge in the form of a growth factor to tweak what the algorithm should receive as the input and give results accordingly. For example, you might already be expecting a new project, a new line of business, or simply more business, and hence more infrastructure cost, but the machine learning system would not know this. With Tanzu CloudHealth forecasting, you can feed in this information and let the machine learning feature do its job of giving you its best forecasts based on the information inputted.
Custom growth factor input
Detailed view of machine learning–powered forecasting
What can ML-powered forecasting enable you to do?
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Improve accuracy of forecasts to establish achievable budgets
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Commit to reservation and savings plans with confidence
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Provide future investment planning and innovation in the cloud
Looking ahead
Cloud cost forecasting is the future of managing cloud. By forecasting your cloud costs, you can gain visibility into your cloud spending, make informed decisions about how to optimize your cloud resources, and reduce unwanted cloud spend.
We at VMware Tanzu CloudHealth hope to continue improving forecasting capabilities with new features in the future. Stay tuned!
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More Content by Anirudh Sareen
