Verda Cloud raises $189M Series B for AI inference infrastructure

Verda Cloud secures major funding for AI compute expansion

Verda Cloud Oy, a Helsinki-based AI infrastructure provider formerly known as DataCrunch, has closed a $189 million Series B funding round to expand its managed AI inference platform. Emergence Capital led the investment, with participation from MUFG Innovation Partners, Supermicro, Varma Mutual Pension Insurance Company, and several other institutional and angel investors, according to details first reported by SiliconANGLE.

The funding brings Verda's total capital raised to more than $450 million across equity and debt financing. The company serves AI workloads for organizations in over 50 countries, from startups to enterprises, including sovereign AI firm Aleph Alpha and visual content company Magnific, which processes millions of inference requests daily on Verda's infrastructure.

Infrastructure built for evolving AI workloads

Verda differentiates itself by building proprietary compilers and serving software rather than simply reselling commodity cloud resources. The company operates an internal AI lab that runs production research workloads to solve engineering challenges including GPU utilization, inference optimization, and kernel engineering. This approach allows Verda to continuously improve performance as new models emerge.

The company plans to operate more than 250 megawatts of capacity in 2027, with data centers live in Finland and expansion underway across Europe, the United Kingdom, the United States, and Asia. Verda is preparing early deployments of Nvidia VR200 NVL72 rack-scale supercomputers in the coming months.

"There's a window right now to build one of the defining compute companies of this generation, and to do so from Europe," said founder and CEO Ruben Byron. "It won't be open for long."

Why it matters

The funding reflects growing enterprise demand for specialized AI inference infrastructure as workloads shift toward longer sessions, multi-turn context accumulation, and agentic use cases. Unlike training-focused cloud providers, Verda targets the inference bottleneck that enterprises face when deploying AI at scale. The company's approach of co-developing with AI labs and open-source projects positions it to adapt quickly as model architectures and optimization techniques evolve. For European enterprises concerned about data sovereignty and latency, Verda offers an alternative to US-dominated hyperscale clouds.

Customer deployments demonstrate technical depth

Epsilon Health trains custom radiology AI models on dedicated Verda clusters using native image resolution. "What we didn't expect was a team of engineers who could help us design our own data streaming and cluster management, not just hand us a cluster and walk away," said Arjun Karpur, Epsilon Health's head of machine learning.

Verda plans to use the new capital to accelerate provisioning and setup times for instant clusters, invest in its AI lab for co-research with other AI developers, and enhance the developer experience with new enterprise features. The company emphasized that today's AI workloads look substantially different than those from a few years ago, requiring infrastructure designed specifically for inference rather than adapted from training environments.

SiliconANGLE first reported the funding details on September 22, 2026.