Verda became Europe’s latest unicorn today, raising $189 myriad in new funding. The oversubscribed Series B was led by Emergence Capital, alongside MUFG Innovation Partners, Supermicro, Varma Mutual Pension Insurance Company, Lifeline Ventures, 6 Degrees Capital (6DC), byFounders, Tesi (Finnish Industry Investment Ltd), and a collection of angel investors including Ola Tørudbakken and Mark Saroufim participating. That brings our total backing to date to complete $450 myriad throughout equity and debt.
"There's a opening correct now to build among the defining compute companies of this generation, and to do so from Europe. It won't be open for long." — Ruben Bryon, originator and CEO
Building the AI Cloud of tomorrow
For the final few years, admission to capability has been a procurement problem. Teams negotiated for months, took shipment of a fixed environment, and organized their workload about any they'd been allocated. That worked whenever capability was scarce and workloads were predictable.
It doesn't anymore. Training runs that formerly took months of advancement preparedness now need to commencement on infrastructure that's already there. Production inference, anywhere latency and preparedness aren't negotiable, needs capability that shows up whenever the workload demands it. Agentic workloads lift the bar equal more, alongside environment that builds throughout many turns and petition that comes in bursts, spiking the instant a tool call returns. Deciding how much capability to get and when, increasingly sits alongside a fistful of engineers, not a procurement squad operating an annual cycle.
Verda is construction the complete stack to encounter that need. We scheme and build our own data centers, the infrastructure inner them, and the phase that runs on top, enabling customers to get the capability how, when, and anywhere they need it, during functioning it at a lesser carbon footprint than the industry average.This lift deepens all three parts of that stack, alongside additional data center capability coming online and deeper funding in the platform, the AI Lab, and the features our customers are already using.
Teams already run their manufacturing AI on Verda
Verda now powers AI workloads for organizations throughout 50+ countries, from early-stage startups to enterprises. We reached a $165 myriad annualized income run charge in July 2026, alongside a squad of complete 250 group throughout Helsinki, London, Taipei, and San Francisco.
Our GPU clusters power Aleph Alpha’s R&D infrastructure, alongside engineers-in-residence collaborating on the underlying application stack. Magnific serves media generation at millions of requests per day on our infrastructure. Epsilon Health trains its radiology AI models on a dedicated Verda cluster, at native depiction resolution.
Our AI Lab lives on the phase our customers use
We build our own compilers and serving software, and do our own achievement and reliability engineering. Our AI Lab is a dedicated squad that runs genuine investigation workloads on the platform, operating on solving difficult engineering problems akin improving GPU utilization, conclusion optimization, and kernel engineering. That activity method customers get additional out of their infrastructure, and it’s what shapes our merchandise roadmap, as the Lab runs into what needs construction before a client has to ask for it.
"What we didn't anticipate was a squad of engineers who could assistance us scheme our own data streaming and collection management, not fair hand us a collection and stroll away." - Arjun Karpur, Head of Machine Learning, Epsilon Health
What’s next
More capacity, and the power to run it: We volition have additional than 250 MW of operations in 2027, alongside data centre capability live in Finland today and additional coming online in Europe, the UK, the US and Asia. That additionally method getting the latest infrastructure to our customers as it’s available. We anticipate early deployments of NVIDIA VR200 NVL72 in the coming months.
Inference, at scale: The workloads customers run today already appearance distinct from a few years ago, longer sessions, environment accumulating complete many turns, and agentic use spiking demand. For the AI teams, the difficulty isn't fair serving one of those sessions well, it's serving thousands of them at formerly without one team's burst becoming another's problem. We are investing in providing admission to models for inference, tuned for how they're used.
Faster provisioning: We're focused on improving provisioning and setup times so they don’t rotate into bottlenecks for AI workloads. That method faster spin-up for instances and clusters, quicker retention attachment, and infrastructure that’s accelerated to react formerly it's running.
Platform updates: We continue to broaden our merchandise portfolio to recommendation AI teams all capability they need throughout the AI lifecycle.Following the latest publish of Container registry, we are on way to publish S3-compatible Object storage. Our Instant clusters fair got Kubernetes alongside Kueue or Slurm via Slinky pre-configured at deployment, which we volition prosecute alongside the long-awaited publish of Managed Kubernetes.
Enterprise readiness: As AI moves from pilots to production, enterprises need to rely it the way they rely item alternatively operating their business. This includes who has admission to it, how it’s protected, and whether that holds up to an audit. We are construction for all three, continuously improving and shipping new capabilities. Recently, we added Audit Logs and SSO for IAM, so enterprises can see who did what and authority who can log in. We enhanced our Confidential computing offering alongside the industry-first assistance of 8× NVIDIA HGX™ B300 and B200, to keep data protected equal during it’s being used. And now recommendation SOC Type II and C5 certifications alongside our existing ones.
Engineering depth: We’re backing the AI Lab to activity closely alongside another AI Labs, open-source projects, and developer tool companies, and to go deeper on co-research alongside the teams already construction on us. That’s how we comprehend what compute gets used for, the example architectures and the optimization techniques shaping genuine workloads today. That understanding feeds into decisions at all flat from data center scheme and hardware choice to provisioning and software.
Developer experience: None of this matters if the group construction on Verda can’t activity the way they already do. That’s why, as we container new features and capabilities, we build for all way a squad works, whether that’s through our console, the CLI or via API and everything in between.
Many of these capabilities are already accessible in the Verda haze platform:
- Try them out in the UI by logging in
- Or study additional concerning our CLI, API, and another provisioning methods on our docs
