Helsinki, Finland-based neocloud outfit Verda Cloud Oy, formerly DataCrunch, today announced it has raised $189 million in early-stage funding to expand its artificial intelligence cloud infrastructure, from AI compute capacity to platform services.
Emergence Capital led the Series B round, with additional investments from MUFG Innovation Partners, Supermicro, Varma Mutual Pension Insurance Co., Lifeline Ventures, 6 Degrees Capital, byFounders and Tesi. A group of angel investors, including Ola Tørudbakken, director of AI systems at Meta Platforms Inc., and Core Automation co-founder Mark Saroufim also joined the round.
The funding brings the company’s total raised to more than $450 million in equity and debt.
Verda aims to provide large-scale managed AI inference to enterprise-level companies where they need it, when they need it, with high optimization and reliability. The company builds its own compilers and serving software and provides its own performance and reliability engineering.
The company’s internal AI lab is dedicated to running real research workloads on the platform, solving real engineering problems such as improving graphics processing unit utilization, inference optimization and kernel engineering. The company says this constant work means that anytime a new model arrives, customers get more out of their infrastructure, and that shapes the 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,” said founder and Chief Executive Ruben Byron. “It won’t be open for long.”
Verda currently plans to operate more than 250 megawatts in 2027, with data center capacity live in Finland and more coming online broadly across Europe, the United Kingdom, the United States and Asia. The company said it’s preparing early deployments of Nvidia VR200 NVL72 rack-scale supercomputers in the coming months.
The neocloud already powers AI workloads for organizations across more than 50 countries, from early-stage startups to enterprises. This includes the sovereign AI development firm Aleph Alpha GmbH’s research and development infrastructure and provides inference for AI-assisted visual content company Magnific’s media generation at millions of requests per day.
Epsilon Health trains its own bespoke radiology AI 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 said it will use the funding to follow customer needs. Today’s workloads look different than a few years ago: longer sessions, context accumulation over many turns and agentic use cases. The challenges of today require faster provisioning and setup times for instant clusters, so the company said it’s investing in its AI lab to work with other AI labs, open-source projects and developer tool companies to build on co-research. This way, the company can learn how compute is used, what model architectures are emerging and which optimization techniques are shaping real workloads today.
The company said it will also focus on the developer experience to ship new features and capabilities that will build enterprise trust, enable new tools and readiness and strengthen the platform’s robustness.
