ScaleOps secures $130M to boost computing efficiency as AI demand surges

As artificial intelligence adoption accelerates, companies are facing a growing but often overlooked problem: inefficient use of expensive computing resources. GPUs frequently remain idle, workloads are over-provisioned, and cloud expenses continue to rise. ScaleOps is positioning itself on the idea that the issue is not a lack of compute but how it is managed.

The startup, which develops software that automatically manages and reallocates computing resources in real time, has raised $130 million in a Series C funding round at an $800 million valuation. The round was led by Insight Partners, with participation from existing investors including Lightspeed Venture Partners, NFX, Glilot Capital Partners, and Picture Capital. According to the company, its platform can reduce cloud and AI infrastructure costs by up to 80%.

ScaleOps was founded in 2022 by Yodar Shafrir, a former engineer at Run: ai, a GPU orchestration company later acquired by Nvidia. Shafrir said his experience working with customers, particularly DevOps teams, revealed how difficult it had become to manage increasingly complex AI workloads.

While platforms like Kubernetes are widely used to run applications across large clusters of machines, they often depend on static configurations that struggle to adapt to rapidly changing demand. This leads to underutilised GPUs, performance bottlenecks, and rising costs.

Shafrir explained that the challenges extend beyond GPUs to include compute, memory, storage, and networking. He observed that similar inefficiencies recurred across organisations, with teams struggling to manage resources effectively in dynamic environments.

In many cases, DevOps teams are forced to coordinate among multiple stakeholders to resolve performance or cost issues, and existing tools typically provide visibility but lack automated solutions. This gap between insight and action has created a significant opportunity in the market.

ScaleOps aims to address this by linking application requirements directly with infrastructure decisions in real time. Its platform is designed to operate autonomously, managing infrastructure end-to-end without requiring constant manual input.

According to Shafrir, the flexibility of Kubernetes is both its strength and its limitation. While it allows for extensive configuration, it also requires continuous manual adjustments to keep up with dynamic application behaviour. He emphasised the need for systems that understand the context of each application, including its performance needs and how those needs evolve.

The company operates in a competitive space alongside players such as Cast AI, Kubecost, and Spot by NetApp. However, Shafrir noted that many automation tools lack full contextual awareness, which can lead to performance issues or downtime, thereby limiting their adoption in production environments.

ScaleOps claims its platform was built specifically for production use, offering a fully autonomous and context-aware system that works without manual configuration. The company believes this approach differentiates it from competitors.

Headquartered in New York, the startup serves enterprise customers worldwide, particularly those running Kubernetes-based infrastructure. Its client base includes major organisations such as Adobe, Wiz, DocuSign, Salesforce, and Coupa, as well as companies across Europe and India.

The Series C funding follows a $58 million Series B round raised in November 2024. Since then, ScaleOps has experienced strong demand for autonomous infrastructure management solutions. The company’s total funding has now reached approximately $210 million.

According to the company, it has achieved more than 450% year-over-year growth and has tripled its workforce over the past year, with plans to expand its team further before year-end.

With the new capital, ScaleOps intends to introduce additional products and enhance its platform capabilities. As demand for AI-driven computing grows, managing infrastructure efficiently is more important than ever, and we're working toward fully autonomous systems capable of handling these challenges at scale.