Cast AI Raises $108M to Optimize AI & Kubernetes Workloads

Optimizing AI Workloads: Cast AI Secures $108 Million Series C

The increasing demand for resources to train and operate artificial intelligence models is creating significant cost and logistical challenges for many organizations. Cast AI, a company specializing in automation tools designed to streamline and optimize workloads – including those for AI – has recently completed a substantial funding round.

Significant Investment and Valuation

Cast AI has successfully raised $108 million in a Series C funding round. These funds will be allocated to further research and development efforts, as well as expansion into key markets, notably the U.S. and other international regions.

Sources indicate that this funding round values the company at approximately $900 million post-money, positioning it as a “near unicorn.” This represents a considerable increase from its previous valuation of $300 million in November 2023.

Focus on Resource Efficiency

“The core challenge revolves around GPUs, computational power, and electricity consumption,” explains Yuri Frayman, CEO and co-founder of Cast AI. “Our primary objective is to maximize efficiency and enable the effective distribution of workloads across available GPUs.”

Prior to this latest round, Cast AI had already secured over $86 million in funding.

Global Presence and Customer Base

While headquartered in Miami, Florida, Cast AI maintains a strong presence in Europe. A significant portion of its development work is conducted in Lithuania, Poland, Romania, and Bulgaria.

The company has rapidly grown its customer base, now serving 2,100 clients over the past three years. Notable customers include Akamai, BMW, FICO, Hugging Face, NielsenIQ, and Swisscom, all leveraging Cast AI’s technology to optimize cloud and on-premise capacity for cost-effective compute workload distribution.

Addressing Resource Shortages

With a current shortage of processors available for AI model training and deployment, effective resource allocation is paramount. Cast AI’s research suggests that, on average, only 10% of CPUs and 23% of memory are actively utilized, a trend that extends to GPU usage as well.

Strategic Partnerships and Investors

The size and composition of this Series C round highlight the company’s ongoing initiatives and collaborative efforts.

G2 Venture Partners and SoftBank Vision Fund 2 are jointly leading the investment. Additional participants include Aglaé Ventures, Hedosophia, Cota Capital, Vintage Investment Partners, Creandum, and Uncorrelated Ventures.

Alignment with Major AI Infrastructure Projects

Frayman emphasizes that the oversubscribed round places Cast AI alongside prominent companies like OpenAI and Crusoe Energy – both of which, along with SoftBank and Oracle, are involved in the expansive Stargate AI infrastructure project in the United States.

Cast AI already collaborates with several of these organizations as both partners and customers. They are actively integrated into Crusoe’s infrastructure and are working with SoftBank to enhance efficiency within their AI data centers.

Furthermore, the startup is contributing to the joint OpenAI and SoftBank initiative to establish services in Japan.

From Cybersecurity to AI Optimization

Although currently focused on AI, Cast AI’s origins lie in a different domain. Founded in 2019 by Yuri Frayman, Leon Kuperman, and Laurent Gil, the company’s founder, Frayman, initially built his career in finance before transitioning to software development.

In 2006, Frayman and Gil co-founded Viewdle, an early machine learning startup that pioneered the use of Nvidia GPUs for training image search classifiers. This early experience provided a foundational understanding of the potential of machine learning.

Viewdle was later acquired by Google.

The founders subsequently developed Zenedge, a cloud-based cybersecurity startup. The challenges they faced in managing cloud costs while scaling Zenedge served as the inspiration for Cast AI.

Evolution of the Platform

Cast AI initially addressed the issue of cloud cost control, particularly for Kubernetes workloads. While Kubernetes applications remain central to the company’s operations and revenue, the recent surge in AI activity has driven significant growth in both customer acquisition and investor interest.

“Cast AI is establishing a new benchmark for cloud efficiency during a period of escalating infrastructure demands,” stated Tim Yap, investment director at SoftBank Investment Advisers.

“Cast was essentially an AI agent before the term became popular,” noted Carl Fritjofsson, general partner at Creandum. “They have been developing this type of automation for a considerable time.”