Miami, Fla-based Cast AI, a startup that leverages machine learning (ML) to assist enterprises in managing their cloud spend, has announced a successful Series B funding round, raising $35 million. The investment, led by Vintage Investment Partners, will be used to further develop Cast AI’s AI offering, providing enterprise teams with a more efficient solution for tracking and optimizing their cloud spend. Cast AI’s technology automates resource management and real-time cost control, helping customers reduce their cloud expenses.
“Every single person at Cast AI is relentlessly focused on helping customers slash their cloud spend by automating tasks that are best performed by machine learning systems.” – Yuri Frayman, Cast AI co-founder and CEO
“That’s why our customer growth continues to accelerate and we’ve welcomed marquee customers.” – Yuri Frayman, Cast AI co-founder and CEO
In today’s digital era, companies are increasingly modernizing their applications and migrating to the cloud. While this transition offers numerous benefits, many teams struggle to effectively manage their cloud bills. As an application scales up, the costs of maintaining it can quickly escalate from thousands to millions of dollars. The primary issue is resource provisioning, in which manual efforts fall short in terms of cost optimization. Recognizing this challenge, the founders of Cast AI, Yuri Frayman, Leon Kuperman, and Laurent Gil, decided to develop an AI-driven solution to eliminate the need for manual optimization.
“We quickly realized that we weren’t alone. Every other company around the entire world that was developing cloud-native applications was in exactly the same boat.” – Laurent Gil, Cast’s chief product officer
Cast AI was launched in 2019 and currently serves multiple enterprise customers, including Akamai, Yotpo, Sharechat, Rollbar, Switchboard, and EVgo. The platform utilizes advanced ML algorithms and heuristics to optimize Kubernetes clusters while providing comprehensive visibility and insights into resource provisioning. Kubernetes, often abbreviated as K8s, automates the deployment and management of containerized applications across on-premises infrastructure or public cloud platforms.
Cast AI distinguishes itself by integrating with cloud partners such as Google Cloud, AWS, or Azure, and running models to analyze and optimize clusters automatically. This level of tuning enables enterprises to achieve over 50% savings on their cloud spend while improving performance, reliability, and engineering productivity. For example, Iterable, one of Cast AI’s customers, has reduced its annual cloud bill by over 60%, amounting to savings of $3-4 million each year.
“With the latest round of funding, Cast AI plans to expand its product and introduce new automated features for Kubernetes optimization.” – Laurent Gil, Cast’s chief product officer
The company has recently rolled out two new features: Workload Rightsizing and PrecisionPack. Workload Rightsizing automates workload scaling in near real-time, ensuring optimal performance and cost-effectiveness. PrecisionPack, on the other hand, employs a sophisticated bin-packing algorithm to strategically position pods on designated nodes, maximizing resource utilization and improving efficiency across Kubernetes clusters.
While Cast AI competes in the FinOps category, aiming to reduce cloud spend, it is not the only player in this space. Other notable companies, including CloudZero, Zesty, and Exostellar, are also challenging the problem with strong backing from venture capital firms.
