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Verda, formerly DataCrunch, raises $189 million Series B for its European AI cloud

Editorial

By TrustList Editorial

Verda, the Helsinki AI cloud formerly called DataCrunch, announced a Series B of $189 million (€163 million) on 22 September 2026, led by Emergence Capital. It will spend on product across its AI cloud and on multiplying its computing capacity.

About Verda, formerly DataCrunch, raises $189 million Series B for its European AI cloud

Verda, formerly DataCrunch, raises $189 million Series B for its European AI cloud

22 September 2026: Verda, an AI cloud provider based in Helsinki, Finland, and previously known as DataCrunch, announced on 22 September 2026 a Series B round of $189 million (€163 million), as stated in its own announcement.

The round

  • Amount and stage: $189 million / €163 million, Series B.
  • Lead investor: Emergence Capital.
  • Other investors named: MUFG Innovation Partners, Supermicro, Varma Mutual Pension Insurance Company, Lifeline Ventures, ENDUR, 6 Degrees Capital, byFounders and Tesi, together with angel investors Ola Torudbakken and Mark Saroufim.
  • Use of the money: product development across every layer of its AI cloud, from computing capacity to platform services, and a large increase in capacity.

What the company does

Verda rents graphics-processor (GPU) computing for AI work: single instances and large clusters for training models, serverless inference for running them, and storage, partly in data centres it runs itself. It positions itself as a European provider, which matters to customers who want AI workloads and data to stay under European jurisdiction.

Why it matters for buyers

Access to GPU capacity remains a constraint for many companies building AI products, and most capacity sits with a handful of US cloud providers. A well-funded European alternative gives buyers another option on price, availability and data location. The participation of Supermicro, a server maker, and a Finnish pension insurer also points to the capital-heavy nature of the business.

What to check

  • AI and engineering teams should compare the exact GPU models on offer, interconnect between nodes for multi-node training, reservation terms and egress charges.
  • Compliance teams should confirm which data centres a workload runs in, who operates them, and which jurisdiction governs the contract.
  • Finance should weigh committed-use discounts against the risk of locking in capacity as hardware generations change quickly.

Company profile on TrustList: Verda

Company profile on TrustList: Varma Mutual Pension Insurance Company

Sources

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