European neocloud provider Verda raises $189 million to build AI infrastructure competing with US hyperscalers and dedicated neoclouds.

Helsinki-based neocloud operator Verda Cloud Oy, formerly known as DataCrunch, has raised $189 million in Series B funding to expand its artificial intelligence cloud infrastructure. Emergence Capital led the round, joined by MUFG Innovation Partners, Supermicro, Varma Mutual Pension Insurance Co., Lifeline Ventures, 6 Degrees Capital, byFounders and Tesi. Angel investors including Ola Tørudbakken, director of AI systems at Meta Platforms Inc., and Core Automation co-founder Mark Saroufim also participated. The funding brings total raised to more than $450 million in equity and debt.

Verda provides large-scale managed AI inference for enterprise customers, building its own compilers, serving software, and performance and reliability engineering. The company operates an internal AI lab that runs research workloads on its platform, tackling engineering challenges such as GPU utilization, inference optimization and kernel engineering. Verda said this work ensures customers benefit from infrastructure improvements whenever new models arrive, directly informing its product roadmap.

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

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

The platform already powers AI workloads for organizations across more than 50 countries, from startups to enterprises. It provides infrastructure for sovereign AI developer Aleph Alpha GmbH's research and development, and handles inference for AI-assisted visual content company Magnific's media generation, processing millions of requests daily. Radiology AI specialist Epsilon Health trains bespoke models on a dedicated Verda cluster 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 Epsilon Health Head of Machine Learning Arjun Karpur.

Verda plans to use the funding to follow evolving customer workloads—longer sessions, context accumulation across turns and agentic use cases—by investing in faster provisioning and instant cluster setup. The company will deepen its AI lab work with other AI labs, open-source projects and developer tool companies through co-research, helping it understand emerging model architectures and optimization techniques. Verda will also prioritize developer experience, shipping features to build enterprise trust, enable new tools and strengthen platform robustness.