GoKawiilAI may be booming, but behind the scenes, companies are wasting vast amounts of expensive compute. GPUs sit idle, workloads are over-provisioned, and cloud costs continue to climb. ScaleOps believes the problem isn’t a shortage — it’s mismanagement.
The startup, which builds software that automatically manages and reallocates computing resources in real-time, has raised $130 million at an $800 million valuation, ScaleOps said Monday. The Series C funding round was led by Insight Partners, with participation from existing investors, including Lightspeed Venture Partners, NFX, Glilot Capital Partners, and Picture Capital. The company says its software reduces cloud and AI infrastructure costs by as much as 80%.
ScaleOps was co-founded in 2022 by Yodar Shafrir, a former engineer at Run:ai, a GPU orchestration startup acquired by Nvidia, after seeing firsthand how difficult it was for companies to manage increasingly complex AI workloads. While tools like Kubernetes help run applications across large clusters of machines, they often rely on static configurations that struggle to keep up with fast-changing demand, leading to underused GPUs, performance issues, and costly inefficiencies.
“As part of my role [at Run:ai], I met many customers, especially DevOps teams,” Shafrir, who is the company’s CEO, told TechCrunch. “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.”
DevOps teams often found themselves chasing down multiple stakeholders to resolve issues, and too often, those efforts fell short. Most existing tools offered visibility into problems, but stopped short of delivering actual solutions. That gap revealed a significant market opportunity.
ScaleOps connects application needs with infrastructure decisions in real time and provides a fully autonomous solution that manages infrastructure end-to-end, Shafrir said.
“Kubernetes is a great system. It’s flexible and highly configurable. But that’s also the problem,” Shafrir said. “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.”
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