Home PC News Cloud optimization startup Cast AI raises $10M

Cloud optimization startup Cast AI raises $10M

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Cast AI, a cloud optimization and management platform, today announced that it raised $10 million in series A funding led by Cota Capital, with Samsung Next and additional investors participating. CEO Yuri Frayman says that the funding will be put toward expanding its sales team and further developing its AI-powered platform.

Cloud adoption exploded during the pandemic, with Forrester forecasting that the public cloud infrastructure market will increase 28% year-over-year to $113.1 billion in 2021. But as enterprises increasingly embrace cloud technologies, they’re devoting larger-than-anticipated portions of their IT budgets to cloud spend. According to one survey, 55% of executives were surprised by cloud costs or experienced sudden cost spikes — many of which weren’t discovered until days, weeks, or even months later.

Based in Miami, Florida, and founded in 2019, Cast leverages AI trained on millions of data points to balance cloud performance and cost. Supporting Amazon Web Services, Google Cloud Platform, and Microsoft Azure, the platform connects to existing clouds and generates a report to identify cost-saving opportunities.

“Cast was created because, during the founders’ previous venture, Zenedge, which was later acquired by Oracle, they experienced enormously high cloud bills and found it hard to understand how to better manage this expense,” Frayman, who cofounded the company with Leon Kuperman and Laurent Gil, told VentureBeat. “They quickly realized that many other business leaders were having similar struggles and that managing cloud is a complex and time-consuming task requiring a lot of expertise. They wanted to lift the burden for other companies, so their engineers could focus on building what they love, and not worrying about infrastructure managing ups and downs.”

Cloud spend analysis

Even before the pandemic, many enterprises were migrating their software operations to the cloud. A 2018 IDG survey found that 73% of organizations had at least one app or a portion of their computing infrastructure already in the cloud. But the challenges both then and today are myriad, with companies responding to a Lemongrass survey by citing security, compliance, costs, and a lack of in-house skills as the top barriers to adoption.

To address the cost concerns, Cast employs a number of heuristics and trained machine learning models to help predict the future state of customers’ apps for the purposes of scaling. For example, the platform has models that attempt to be proactive with the scaling requirements for each cloud cluster under management. If the models — which are customer-specific — predict incorrectly, Cast uses the errors as additional data points to improve the model.

“We have other models that use global datasets for market characteristic predictions,” Frayman explained. “For example, we train a global model to predict instance preemptions by machine type, region, availability zone, and seasonality. This model is shared autonomously across all customers, and all the data is used to retrain the model continuously. One of the variables of our global model started by benchmarking more than 500 different instance types in four cloud providers in all regions across the world. This helps the global model to understand and contrast instances and how much compute quantity you get per virtual machine type.”

Cast competes with Kubecost, Spotinst, and Granulate in the cloud optimization solutions market, among others. But the company claims to have more than two dozen customers across the adtech, ecommerce, fintech, and data science industries.

Cast, which has raised $18 million in venture capital to date, has a workforce of 52 employees and plans to have at least 80 employees by the end of the year.

“Cloud resources should come from any provider, and customers should leverage the best cost and performance resources available in real time,” Frayman said. “This cannot be a human-driven decision in order to scale.”

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