What $189M in funding unlocks for Verda customers

Verda raises $189M in Series B Verda raises $189M in Series B
Learn more about Verda’s Series B funding

AI Cloud
GPUs
Solutions
Resources
About Pricing Contact sales Contact us Log in Sign up AI Cloud From rapid prototyping to foundation training to scalable inference Explore all features Compute GPU instances Fast access to the latest GPU instances from NVIDIA Instant clusters Self-service GPU clusters with InfiniBand interconnect Serverless Serverless containers Auto-scaling GPU containers for inference and batch jobs Storage Block storage High-speed NVMe virtual disks Shared filesystem POSIX-compliant storage Container registry OCI-compliant image storage GB300 NVL72 New 1x tray to 2+ racks · NVLink v5 See full specs B300 SXM6 30 CPUs 255 GB RAM 268 GB VRAM B200 SXM6 30 CPUs 170 GB RAM 180 GB VRAM RTX PRO 6000 30 CPUs 90 GB RAM 96 GB VRAM H200 SXM5 44 CPUs 170 GB RAM 141 GB VRAM H100 SXM5 30 CPUs 120 GB RAM 80 GB VRAM A100 SXM4 22 CPUs 120 GB RAM 80 GB VRAM Solutions AI Lab In-house AI Lab, contributing to frontier research and open-source projects Confidential computing Hardware-attested inference and fine-tuning Resources Blog GPU benchmarks and R&D on AI Docs Technical documentation and tutorials API Control GPU resources via external code Community Forum Exchange knowledge with AI experts Trust center Security and compliance at Verda About Company Learn more about us as a company Careers Join us to shape the future of AI Jobs Explore the open positions Newsroom Stay up to date with our latest announcements AI Cloud From rapid prototyping to foundation training to scalable inference Explore all features Compute GPU instances Fast access to the latest GPU instances from NVIDIA Instant clusters Self-service GPU clusters with InfiniBand interconnect Serverless Serverless containers Auto-scaling GPU containers for inference and batch jobs Storage Block storage High-speed NVMe virtual disks Shared filesystem POSIX-compliant storage Container registry OCI-compliant image storage GPUs GB300 NVL72 New 1x tray to 2+ racks · NVLink v5 See full specs B300 SXM6 30 CPUs 255 GB RAM 268 GB VRAM B200 SXM6 30 CPUs 170 GB RAM 180 GB VRAM RTX PRO 6000 30 CPUs 90 GB RAM 96 GB VRAM H200 SXM5 44 CPUs 170 GB RAM 141 GB VRAM H100 SXM5 30 CPUs 120 GB RAM 80 GB VRAM A100 SXM4 22 CPUs 120 GB RAM 80 GB VRAM Solutions AI Lab In-house AI Lab, contributing to frontier research and open-source projects Confidential computing Hardware-attested inference and fine-tuning Resources Blog GPU benchmarks and R&D on AI Docs Technical documentation and tutorials API Control GPU resources via external code Community Forum Exchange knowledge with AI experts Trust center Security and compliance at Verda About Company Learn more about us as a company Careers Join us to shape the future of AI Jobs Explore the open positions Newsroom Stay up to date with our latest announcements Pricing Log in Sign up Platform updates What $189M in funding unlocks for Verda customers Sep 22, 2026 | Ömer Tümer | 6 min read

Share
Building the AI Cloud of tomorrowIndexBuilding the AI Cloud of tomorrowTeams already run their production AI on VerdaOur AI Lab lives on the platform our customers useWhat’s next Verda became Europe’s latest unicorn today, raising $189 million in new funding. The oversubscribed Series B was led by Emergence Capital, with MUFG Innovation Partners, Supermicro, Varma Mutual Pension Insurance Company, Lifeline Ventures, 6 Degrees Capital (6DC), byFounders, Tesi (Finnish Industry Investment Ltd), and a group of angel investors including Ola Tørudbakken and Mark Saroufim participating. That brings our total funding to date to over $450 million across equity and debt. "There's a window right now to build one of the defining compute companies of this generation, and to do so from Europe. It won't be open for long." — Ruben Bryon, founder and CEO
Click
here

to accept cookies and watch the video on this website or watch on YouTube.
You can watch this video on YouTube
.
Building the AI Cloud of tomorrow For the last few years, access to capacity has been a procurement problem. Teams negotiated for months, took delivery of a fixed environment, and organized their workload around whatever they'd been allocated. That worked when capacity was scarce and workloads were predictable. It doesn't anymore. Training runs that once took months of advance planning now need to start on infrastructure that's already there. Production inference, where latency and availability aren't negotiable, needs capacity that shows up when the workload demands it. Agentic workloads raise the bar even more, with context that builds across many turns and demand that comes in bursts, spiking the moment a tool call returns. Deciding how much capacity to get and when, increasingly sits with a handful of engineers, not a procurement team working an annual cycle. Verda is building the full stack to meet that need. We design and build our own data centers, the infrastructure inside them, and the platform that runs on top, enabling customers to get the capacity how, when, and where they need it, while operating it at a lower carbon footprint than the industry average.This raise deepens all three parts of that stack, with more data center capacity coming online and deeper investment in the platform, the AI Lab, and the features our customers are already using. Teams already run their production AI on Verda Verda now powers AI workloads for organizations across 50+ countries, from early-stage startups to enterprises. We reached a $165 million annualized revenue run rate in July 2026, with a team of over 250 people across Helsinki, London, Taipei, and San Francisco. Our GPU clusters power Aleph Alpha’s R&D infrastructure, with engineers-in-residence collaborating on the underlying software 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 image resolution. Our AI Lab lives on the platform our customers use We build our own compilers and serving software, and do our own performance and reliability engineering. Our AI Lab is a dedicated team that runs real research workloads on the platform, working on solving hard engineering problems like improving GPU utilization, inference optimization, and kernel engineering. That work means customers get more out of their infrastructure, and it’s what shapes our product roadmap, as the Lab runs into what needs building before a customer has to ask for it. "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." - Arjun Karpur, Head of Machine Learning, Epsilon Health What’s next More capacity, and the power to run it: We will have more than 250 MW of operations in 2027, with data centre capacity live in Finland today and more coming online in Europe, the UK, the US and Asia. That also means 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 look different from a few years ago, longer sessions, context accumulating over many turns, and agentic use spiking demand. For the AI teams, the challenge isn't just serving one of those sessions well, it's serving thousands of them at once without one team's burst becoming another's problem. We are investing in providing access 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 become bottlenecks for AI workloads. That means faster spin-up for instances and clusters, quicker storage attachment, and infrastructure that’s fast to respond once it's running. Platform updates: We continue to broaden our product portfolio to offer AI teams every capability they need across the AI lifecycle.Following the recent release of Container registry, we are on path to release S3-compatible Object storage. Our Instant clusters just got Kubernetes with Kueue or Slurm via Slinky pre-configured at deployment, which we will follow with the long-awaited release of Managed Kubernetes. Enterprise readiness: As AI moves from pilots to production, enterprises need to trust it the way they trust anything else running their business. This includes who has access to it, how it’s protected, and whether that holds up to an audit. We are building 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 control who can log in. We enhanced our Confidential computing offering with the industry-first support of 8× NVIDIA HGX™ B300 and B200, to keep data protected even while it’s being used. And now offer SOC Type II and C5 certifications alongside our existing ones. Engineering depth: We’re funding the AI Lab to work closely with other AI Labs, open-source projects, and developer tool companies, and to go deeper on co-research with the teams already building on us. That’s how we understand what compute gets used for, the model architectures and the optimization techniques shaping real workloads today. That understanding feeds into decisions at every level from data center design and hardware selection to provisioning and software. Developer experience: None of this matters if the people building on Verda can’t work the way they already do. That’s why, as we ship new features and capabilities, we build for every way a team works, whether that’s through our console, the CLI or via API and everything in between. Many of these capabilities are already available in the Verda cloud platform: Try them out in the UI by logging in Or learn more about our CLI, API, and other provisioning methods on our docs These might also interest you
See all
Company news 22.9.2026 Verda raises $189M/€163M to accelerate development of its full-stack AI cloud and multiply compute capacity Verda has raised $189 million (€163 million) in the latest funding round led by Emergence Capital. This brings Verda's total funding to over $450M (€390M). Platform updates 10.9.2026 New in Verda’s Instant clusters: Slurm on Kubernetes, Audit logs, Health checks Kubernetes, Slinky, Kueue, layered health checks, audit logs, container registry, and provisioning methods: What's new in Verda’s Instant clusters over 2026. Monthly digests 11.8.2026 Verda Monthly Digest: July Edition July recap: audit logs, Google SSO, a new CFO, and Verda on the ground across Paris, Seoul, and Las Vegas.
Sign up to our newsletter
Subscribe
Fill this required field with your name
You are subscribed. We could not subscribe you. Please try again. Products GPU instances Instant clusters Serverless containers Block storage Shared filesystem Container registry GPU Models GB300 NVL72 B300 B200 H200 H100 A100 RTX PRO 6000 Solutions AI Lab Confidential computing Resources Blog Docs API Community Trust center Status Release notes About Contact us Pricing Company Careers Jobs Newsroom

Terms and Conditions Privacy Policy Cookie Policy
Manage cookies
© Verda 2026

Verda recently secured $189 million in Series B funding, bringing the company's total funding to over $450 million across equity and debt, driven by investors including Emergence Capital and several other participants. This capital is allocated toward building the full-stack AI cloud of tomorrow, focusing on addressing the evolving demands of AI workloads, which have shifted from predictable batch processing to dynamic, bursty, and agentic workloads requiring scalable infrastructure. The company emphasizes its role in designing, building, and operating its own data centers and platform to provide customers with the necessary capacity, flexibility, and efficiency while maintaining a lower carbon footprint than the industry average.

Verda currently powers AI workloads for organizations across more than fifty countries, serving a diverse clientele ranging from early-stage startups to large enterprises. Their infrastructure supports significant applications, including powering the research and development of Aleph Alpha through GPU clusters and enabling Epsilon Health to train radiology AI models on dedicated infrastructure. This operational scope demonstrates the platform's utility in demanding scenarios, such as powering media generation or complex model training at native image resolution.

A core aspect of Verda’s strategy involves integrating deep engineering within its operations, exemplified by the in-house AI Lab. This lab functions as a dedicated team working on solving fundamental engineering challenges, such as improving GPU utilization, inference optimization, and kernel engineering. This internal research directly informs the product roadmap, ensuring that infrastructure decisions, from data center design to provisioning software, are rooted in real-world workload requirements. This iterative process allows Verda to develop custom compilers and serving software, moving beyond simply supplying clusters to actively managing the underlying systems.

The platform encompasses a wide range of compute, storage, and deployment capabilities. This includes access to the latest NVIDIA GPUs, such as the H100, A100, B200, and RTX PRO 6000, delivered via instant clusters and flexible compute options. Storage solutions feature block storage, high-speed NVMe virtual disks, shared filesystems, and OCI-compliant container registries. Furthermore, the platform supports serverless containers and auto-scaling GPU containers for inference and batch jobs.

Looking ahead, Verda plans significant expansion in capacity, targeting over 250 megawatts of operations by 2027 across Europe, the UK, the US, and Asia, with phased deployment of the latest infrastructure, such as NVIDIA VR200 NVL72. The focus for future development centers on scaling inference capabilities for increasingly complex workloads that involve accumulating context over many turns and handling burst demands from agentic applications. This requires advancements in provisioning speed, focusing on quicker spin-up times for instances and clusters.

To enhance enterprise readiness, Verda is strengthening security and compliance features. They are implementing advanced controls, including Audit Logs and Single Sign-On for IAM, to provide enterprises with visibility and control over access. Confidential computing is advanced through support for NVIDIA HGX B300 and B200, which protect data during use, alongside offering SOC Type II and C5 certifications to meet rigorous enterprise standards. Continuous improvement in the developer experience is also paramount, ensuring that all new features and capabilities are accessible through intuitive console interfaces, command-line interfaces, and APIs. The ongoing investment in the AI Lab facilitates deeper collaboration with the broader AI community, open-source projects, and research teams, ensuring that the platform continuously evolves to meet the needs of AI engineers and researchers.