As enterprises go through digital transformation to improve efficiency, increase values and innovation, the adoption of cloud and Kubernetes has accelerated. However, there are many challenges. One of the major concerns of moving applications and services to the cloud is the cost. Managing cloud costs has become a challenging task for businesses and organizations. Gartner published a report on this topic and proposed a well-defined framework for managing and optimizing the costs of public cloud services [1].

Among the key findings in this report is that most organizations are not prepared to profit from the savings opportunity of efficient use of cloud services and are likely to overspend. The report lists a series of recommendations and a Guidance Framework to manage cloud spending on an ongoing basis. Five distinct areas are defined by the framework: Plan, Track, Reduce, Optimize and Evolve. It provides a logical flow on developing and implementing capabilities in managing cloud spending.

ProphetStor’s Federator.ai utilizes machine-learning technologies as a unique approach to help organizations solve the cloud overspending problem. In this article, we demonstrate how Federator.ai’s solution implements many of the recommendations suggested by Gartner’s Guidance Framework that can benefit customers using SUSE Rancher-managed clusters. Mainly, with the ability to forecast the resource usages based on the past operational metrics, Federator.ai makes use of the predicted resource usage in cost/budget planning, tracking of both past usages and predicted future usages, reducing the cost of applications by right-sizing the resource allocation and optimizing the performance and cost with intelligent horizontal pod autoscaling.