Miami, Fla-based Cast AI, a startup that taps machine learning (ML) to help enterprises bring their cloud spend under control, today announced it has raised $35 million in a series B round of funding.

The investment, led by Vintage Investment Partners, will be used by the company to build out its AI offering and give enterprise teams a more capable solution to not only track their cloud spend but also optimize it according to needs. It completely automates the manual job of managing resources in real time and keeping costs on the lower side.

“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 said in a statement. “That’s why our customer growth continues to accelerate and we’ve welcomed marquee customers.”

Today, nearly every company with any sort of captureable digital data is modernizing applications and moving them to the cloud.

The shift is natural — given the obvious advantages from hyperscalers, but many teams find it hard to get a grip on their cloud bills.

As their application scales up, the expense of keeping the whole thing running goes from thousands of dollars to millions. And the reason is: over or under-provisioning of resources. The manual effort to manage the resources just doesn’t work well enough to save money. 

When Yuri Frayman, Leon Kuperman, and Laurent Gil, the founders of Oracle-acquired cybersecurity platform Zenedge, observed the same problem with their product, they decided to have AI take over and eliminate the need for manual optimization. This led to the launch of their second venture: Cast AI.

“We quickly realized that we weren’t alone,” said Gil, Cast’s chief product officer, in an interview with VentureBeat. “Every other company around the entire world that was developing cloud-native applications was in exactly the same boat. Our goal [with Cast AI] was to build the product we wished we had at Zenedge. But it had to be something more than a simple cost observability tool. We needed to create an advanced AI platform capable of scaling cloud resources up and down, in real-time, while optimizing for cost at the same time.”

The founders launched the company in 2019 and are currently serving multiple enterprise customers, among them Akamai, Yotpo, Sharechat, Rollbar, Switchboard and EVgo.

At the core, the offering can be described as an all-in-one platform that utilizes advanced ML algorithms and heuristics to automatically optimize Kubernetes clusters while providing full visibility and insights into how the resources are provisioned. 

Often abbreviated as K8s, Kubernetes automates the deployment and management of containerized applications using on-premises infrastructure or public cloud platforms. When multiple versions of this system are in use, it’s a Kubernetes cluster at play. 

Now, at a time when most organizations focus on automated monitoring tools for their K8s clusters, Cast AI goes a step ahead by plugging via cloud partners (Google Cloud, AWZ or Azure) and running models to automatically analyze and optimize these clusters.

This level of tuning allows enterprises to save 50% or more on their cloud spend, boosting performance, reliability, DevOps and engineering productivity, 

For instance, one customer, Iterable, was able to reduce its annual cloud bill by over 60% – translating into savings worth $3-4 million every year, Gil said.

With the latest round of funding, which takes Cast AI’s total capital raised to $73 million, the company plans to expand its product and automate more aspects of Kubernetes optimization. In fact, it just launched two new features on the platform: Workload Rightsizing and PrecisionPack. 

The former automates the scaling of workload requests in near real-time, ensuring optimal performance while being cost-effective. Meanwhile, the latter is the next-generation Kubernetes scheduling approach that eradicates randomness in pod placement. It employs a sophisticated bin-packing algorithm to ensure strategic pod positioning onto the designated set of nodes, maximizing resource utilization, while bolstering efficiency and predictability across Kubernetes clusters.

While Cast AI is a strong contender in the so-called FinOps category – tools trying to bring down cloud spend, it is not the only one working to target this problem. Players like CloudZero, Zesty and Exostellar are also moving aggressively in the same space, thanks to strong venture capital backing.