The AI sector is experiencing rapid growth, yet a hidden inefficiency plagues many organizations: the wasteful use of valuable computing power. Graphics processing units (GPUs) frequently remain inactive, workloads are allocated more resources than necessary, and cloud expenses persistently rise. ScaleOps argues the core issue is not a lack of resources but rather poor management. The startup, which develops software to automatically manage and reallocate computing resources in real time, announced on Monday that it has secured $130 million in funding, achieving an $800 million valuation.

This Series C investment was spearheaded by Insight Partners. Existing investors also participated, including Lightspeed Venture Partners, NFX, Glilot Capital Partners, and Picture Capital. According to the company, its software can slash 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 startup later acquired by Nvidia. Shafrir's experience revealed the significant challenges companies face in managing increasingly complex AI workloads. While platforms like Kubernetes are effective for running applications across large machine clusters, they often depend on static configurations. These configurations struggle to adapt to rapidly shifting demands, resulting in underutilized GPUs, performance bottlenecks, and expensive inefficiencies.

“While they really liked what Run:ai provided, they still struggled to manage their production workloads, especially as inference workloads became more common in the AI era. When I zoomed out, I realized the problem wasn’t just GPUs. It extended to compute, memory, storage, and networking. The same patterns kept repeating; teams were failing to manage resources efficiently,” Shafrir explained.

DevOps teams frequently found themselves coordinating with multiple stakeholders to troubleshoot issues, often without achieving a satisfactory resolution. Most available tools could identify problems but fell short of implementing actual fixes. This gap highlighted a substantial market opportunity. ScaleOps aims to bridge it by connecting application requirements with infrastructure decisions in real time, offering a fully autonomous, end-to-end management solution.

“Kubernetes is a great system. It’s flexible and highly configurable. But that’s also the problem,” Shafrir noted. “Kubernetes relies heavily on static configurations. Applications today are highly dynamic, which requires constant manual work across teams. You need something that understands the context of each application—what it needs, how it behaves, and how the environment is changing.”

The market includes several other players, such as Cast AI, Kubecost, and Spot. According to the CEO, while many companies offer automation tools, these often operate without full context, which can lead to performance issues or even downtime, eroding trust among teams responsible for production environments. ScaleOps asserts its platform was designed from inception specifically for production use. It is fully autonomous, context-aware, and works immediately without manual setup—capabilities the company believes set it apart from competitors.

Headquartered in New York, ScaleOps serves enterprise customers worldwide, particularly those operating Kubernetes-based infrastructure. Its client base includes large organizations as well as companies across Europe and India. The platform is used by a range of enterprise clients, including Adobe, Wiz, DocuSign, Salesforce, and Coupa.

This Series C funding arrives approximately a year and a half after ScaleOps raised $58 million in its Series B round in November 2024. Since then, demand for autonomous cloud infrastructure management solutions has been strong, Shafrir said, adding that the company is still in the early phases of its expansion. A spokesperson confirmed the company’s total funding now stands at about $210 million.

ScaleOps reported more than 450% year-over-year growth and has tripled its employee count over the past 12 months, with plans to more than triple it again by the end of this year. The new capital will support the rollout of new products and further platform expansion. As AI continues to fuel demand for computing power, the ability to manage that infrastructure efficiently is becoming ever more crucial. The startup stated it will continue its development toward fully autonomous infrastructure management.