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Reserved.ai has added a new artificial intelligence (AI) tool to more accurately predict cloud computing workloads and rein in cloud spending. The company says its Purchase Planner tool uses machine learning to optimize cloud computing workloads by automatically purchasing cloud reserve instances based on the forecasts it generates.
Purchase Planner leverages the machine learning algorithms Reserved.ai created to monitor and forecast when organizations should optimally reserve instances on a public cloud. The forecasts are based on customers’ current application workload requirements, as well as what the AI platform estimates overall demand for reserved instances is likely to be in the months ahead.
The Purchase Planner tool is currently available on Amazon Web Services (AWS), and there is also a public beta edition available for Microsoft Azure clouds with support for Google Cloud Platform (GCP) planned for this quarter.
Reserved.ai CEO Aran Khanna said Purchase Planner enables Reserved.ai to automatically purchase reserved instances on the AWS cloud. If, for any reason, those reserved instances are not required, Khanna said Reserved.ai will buy them back from customers.
Given thriving demand for reserved instances on public cloud, Khanna said reselling those instances minimizes any financial risk for the company.
Historically, many IT organizations have over-provisioned their cloud resources to mitigate the cost of having to purchase cloud instances on demand. In the wake of the economic downturn brought on by the COVID-19 pandemic, Khanna said more IT teams are attempting to optimize their cloud costs by reducing the number of reserved cloud instances they anticipate they’ll need based on their current cloud infrastructure consumption. Achieving that goal requires a massive amount of guesswork that can be more accurately calculated by machine learning algorithms, Khanna said.
Khanna said the Reserved.ai platform provides the added benefit of requiring only 15 minutes to install, with a minimal infrastructure footprint required.
Capacity optimization as an IT discipline has been around for decades. In the age of the cloud, however, IT organizations lost visibility into capacity utilization and cloud computing costs as developers spun up virtual machines. Now, finance teams require IT teams to, at the very least, maximize the number of workloads run while more accurately predicting cloud costs. Any actual reduction in cloud spending is a secondary benefit. The challenge today is that most cloud optimization efforts rely on the manual efforts of IT teams, and often, they’re unable to keep pace with the rapid rate of change in the cloud.
Understanding the true cost of cloud computing will be more critical in the months ahead, as more organizations look to compare the cost of running one type of workload on the cloud versus another. Gaining visibility into those costs today will only be feasible when IT teams can rely on AI platforms capable of analyzing those costs in near real time. Otherwise, by the time an IT team becomes aware of an opportunity to reduce costs, the opportunity has already been seized by another organization.
Regardless of how IT teams rein in cloud costs in the months ahead, the goal should be to maximize the number of workloads that can run without increasing the budget. After all, most organizations today are focused on maximizing their existing cloud investments to the fullest extent possible.
