Cast AI Raises $108M to Optimize AI Workloads and Cloud Efficiency

The rapid growth in AI training and inference workloads has created significant challenges for organizations in managing cloud resources and controlling costs. Cast AI, a startup specializing in workload automation and optimization, has raised $108 million in a Series C funding round, bringing its valuation close to $900 million. This capital injection will fuel further research and development as well as expansion into key markets such as the U.S.

Founded in 2019 and headquartered in Miami with a strong European development presence, Cast AI has attracted over 2,100 customers including industry leaders like BMW, Akamai, and HuggingFace. The company’s platform integrates seamlessly with all major cloud providers and on-premises systems to optimize GPU, CPU, and memory utilization, which research shows is often under 25% in typical deployments.

Cast AI’s technology addresses the critical bottleneck of compute resource scarcity by automating workload distribution and enhancing efficiency across heterogeneous cloud environments. This capability is especially vital as AI models demand increasing GPU power and energy consumption, driving up operational expenses for enterprises.

The Series C round was co-led by G2 Venture Partners and SoftBank Vision Fund 2, with participation from Aglaé Ventures, Hedosophia, and others. These investors connect Cast AI to a broader AI ecosystem including OpenAI and Crusoe Energy, with whom it collaborates on large-scale AI infrastructure projects like Stargate in the U.S. and AI service development in Japan.

The founders’ deep expertise in machine learning and cloud cost management underpins Cast AI’s mission. CEO Yuri Frayman, along with co-founders Leon Kuperman and Laurent Gil, previously developed early GPU-accelerated machine learning applications and cloud cybersecurity solutions. Their experience with cloud cost challenges directly inspired Cast AI’s focus on Kubernetes workload efficiency and AI-driven automation.

As AI infrastructure demands surge globally, Cast AI is positioned to set new standards for cloud efficiency. Its platform enables enterprises to maximize resource utilization, reduce energy consumption, and scale AI workloads cost-effectively. This aligns with growing industry needs for sustainable and scalable AI deployment strategies.

In summary, Cast AI’s recent funding milestone highlights the critical importance of intelligent automation in managing AI workloads amid rising compute costs and resource constraints. Its partnerships with leading AI and cloud infrastructure players underscore the growing ecosystem collaboration necessary to meet the demands of next-generation AI applications.