Is Verda worth it for European AI startups?

Yes, if you need access to the latest NVIDIA GPUs (GB300 NVL72, B200, H200) with low-latency InfiniBand and direct engineering support. Verda's SOC 2 Type II compliance and confidential computing also help with enterprise deals. However, pricing requires a sales call, so factor that into your decision.

Does Verda integrate with Terraform?

Yes, Verda has a first-class Terraform provider for managing instances, clusters, storage, and serverless containers. They also support OpenTofu and provide a dedicated CLI.

How does Verda compare to Lambda Labs?

Verda offers newer and more diverse GPU options (GB300 NVL72, B200, H200) and a full-stack platform with serverless and managed inference, whereas Lambda Labs focuses on GPU cloud with public pricing. Verda is better for EU data sovereignty and confidential computing, but lacks transparent pricing.

What is the cheapest Verda tier?

Verda does not currently disclose any pricing tiers publicly; all plans require contacting sales. There is no free tier either. For the lowest cost GPU cloud with public pricing, consider alternatives like Lambda Labs or Vast.ai.

What are Verda's biggest limitations?

Main limitations: pricing is hidden behind sales calls, no free tier, managed inference limited to image (FLUX) and audio (Whisper) models only (no text LLM endpoints), and global availability is currently EU-focused with US/APAC expansion planned for H2 2026.

Can Verda replace AWS for GPU training?

For GPU-intensive training, yes, Verda can replace AWS if you need the latest NVIDIA GPUs (GB300, B200) with InfiniBand clusters and prefer EU hosting. However, Verda lacks AWS's breadth of managed services (SageMaker, database, analytics), so it's best as a specialized compute layer rather than a full cloud replacement.

How long does Verda take to set up?

GPU instances and Instant Clusters (16x-128x GPUs) deploy in minutes via web console or API. Serverless containers take about 10 minutes using the quickstart guide. Bare-metal clusters require 1-3 business days for custom configuration after contacting sales.

How do I migrate from Runpod to Verda?

Verda provides a quickstart guide for migrating from Runpod. Containerized workloads can be moved with minimal changes by pushing your Docker image to Verda's container registry and deploying as a serverless container or instance. See the official docs for step-by-step instructions.

Is Verda good for LLM training?

Yes, Verda is excellent for LLM training. You can provision Instant Clusters with up to 128 GPUs connected via InfiniBand, use Slurm or Kubernetes, and access the latest NVIDIA GPUs like H200, B200, and GB300 NVL72. The platform also offers Terraform automation and direct engineering support for optimization.

DataCrunch

Paid

European full-stack AI cloud for GPU instances and serverless inference

By Tanmay Verma, Founder · Last verified 21 Jun 2026

4.3k views

Added 26d ago

85/100Safe Bet

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In short

DataCrunch — European full-stack AI cloud for GPU instances and serverless inference. Best for European AI startups needing the latest NVIDIA GPUs, Enterprises requiring confidential computing and SOC 2 compliance, Teams wanting a full-stack AI cloud with serverless and managed endpoints. Paid pricing.

Affiliate disclosure: We earn a commission when you use our links. Editorial picks are independent. How we choose.

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Editorial Verdict

Best for

European AI startups needing the latest NVIDIA GPUsEnterprises requiring confidential computing and SOC 2 complianceTeams wanting a full-stack AI cloud with serverless and managed endpointsResearchers needing low-latency GPU clusters with InfiniBand

Not ideal for

Teams needing global multi-region deployment outside EU/US/APAC H2 2026Users seeking cheapest GPU cloud with public pricingProjects requiring legacy GPUs not offered (e.g., T4, V100)Those wanting extensive pre-built AI services like SageMaker or Vertex AI

Verda is a strong pick for European AI teams needing cutting-edge NVIDIA GPUs with low latency and full-stack support. Its SOC 2 Type II compliance, confidential computing, and direct engineering access make it enterprise-ready. However, global availability is still limited to EU, US, and planned APAC expansion, and pricing requires contacting sales—no public tiers or free tier. If you need a simple, pay-as-you-go GPU cloud with public pricing, consider Lambda Labs or Vast.ai instead.

Compare with: DataCrunch vs CoreWeave, DataCrunch vs Deci, DataCrunch vs Tavily

Last verified: June 2026

Behind the Verdict

Verda stands out as a vertically integrated European AI cloud with a focus on cutting-edge NVIDIA hardware (GB300 NVL72, B200, H200) and full-stack capabilities. Its strengths include instant clusters with InfiniBand for distributed training, serverless containers for auto-scaling inference, and managed endpoints for popular models like FLUX and Whisper. The recent $117M raise and SOC 2 Type II audit signal financial and security maturity. The partnership with ExpressVPN for confidential computing is a differentiator for sensitive workloads. Weaknesses: pricing is not transparent—all plans require contacting sales, which may frustrate individuals or small teams. There is no free tier, and managed inference is limited to image and audio models (no text LLM endpoints yet). Global presence is currently EU-focused, with US and APAC expansion planned for late 2026. If you need a simple, low-cost GPU cloud with public pricing, Lambda Labs or Vast.ai might be better. For European enterprises needing the latest GPUs, compliance, and hands-on support, Verda is a top contender.

Skip DataCrunch if Skip Verda if you need a low-cost GPU cloud with transparent, public pricing and a free tier—pricing requires a sales call.

Latest from DataCrunch

Updated today

Across the latest 5 updates: 1 launch, 2 changelog entries and 2 news mentions.

ChangelogBlog·9 days agoNewest

Verda Monthly Digest: May Edition

Monthly digest covering May 2026 updates.

NewsBlog·19 days ago

Verda to deploy NVIDIA VR200 NVL72 and R200 in H2 2026 across Europe, the US, and APAC

Verda plans global deployment of NVIDIA VR200 NVL72 and R200 in H2 2026.

ChangelogBlog·May 8

Verda Monthly Digest: April Edition

Monthly digest covering April 2026 updates.

NewsBlog·Apr 30

Verda raises $117M to build the next generation of AI cloud infrastructure

Raised $117M to expand AI cloud infrastructure globally.

LaunchBlog·Apr 23

Verda completes SOC 2 Type II audit

Completed SOC 2 Type II audit, enhancing security and compliance.

Viability Score

85/100

Safe Bet

How likely is DataCrunch to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.

momentum

100

funding runway

website health

github activity

wrapper dependency

100

Last calculated: June 2026

How we score →

About DataCrunch

Verda (formerly DataCrunch) is a European full-stack AI cloud platform providing on-demand GPU instances, instant clusters (16x-128x GPUs via InfiniBand), bare-metal clusters, serverless containers with auto-scaling, and managed inference endpoints. It covers the entire AI lifecycle from training to inference, with a vertically integrated stack—own data centers, in-house AI lab, and direct engineering support. Verda recently raised $117M, achieved SOC 2 Type II compliance, and partners with ExpressVPN for confidential computing. It offers NVIDIA GPUs from A100 to the latest GB300 NVL72 and B200, plus plans to deploy VR200 NVL72 and R200 in H2 2026 across Europe, US, and APAC. Verda emphasizes EU data sovereignty, renewable energy, and low-latency performance for European AI teams.

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Key Features

On-demand GPU instances with PAYG pricing
Instant clusters (16x-128x GPUs via InfiniBand)
Bare-metal clusters configured to specs
Serverless containers with auto-scaling
Managed endpoints for SOTA image and audio models
Confidential computing with hardware attestation
SOC 2 Type II compliance
NVIDIA GB300 NVL72, B200, H200, H100, A100, and more
Arm AGI CPU paired with GB300 for agentic workloads
EU data sovereignty and renewable energy data centers
Web console, API, SDK, Terraform, OpenTofu, MCP Server
Container registry, batch jobs, block/shared/object storage
NVLink and RoCE networking
In-house AI lab for co-research and optimization
Compute Credits program for earning free compute

Real-world workflow fit

Concrete scenarios for the personas DataCrunch actually fits — and what changes day-one when you adopt it.

ML researcher

You need to train a 70B LLM on a 64-GPU cluster with InfiniBand for 3 weeks.

Outcome: Provision an Instant Cluster via web console, get 64x H200 GPUs with InfiniBand in minutes, use Slurm for job scheduling, and complete training faster than spot instances.

AI startup CTO

You want to deploy a FLUX image generation API with auto-scaling and low latency for production.

Outcome: Use serverless containers to deploy FLUX.2 with auto-scaling, pay per request, and serve millions of images daily without managing infrastructure.

Enterprise security lead

You need to run sensitive NLP workloads on GPU with hardware-attested confidentiality.

Outcome: Spin up confidential computing instances with NVIDIA confidential computing and ExpressVPN integration, auditable via SOC 2 Type II, meeting compliance requirements.

Use Cases

Distributed training of large language models on a 128-GPU InfiniBand cluster
Serverless inference for real-time image generation with FLUX.2
Confidential AI workloads using hardware-attested compute with ExpressVPN
Multi-node GPU clusters for reinforcement learning research
Migrate containerized ML workloads from Runpod to Verda with minimal changes
Batch audio transcription using Whisper with auto-scaling containers

Models Under the Hood

NVIDIA GB300 NVL72NVIDIA B300 SXM6NVIDIA B200 SXM6NVIDIA H200 SXM5NVIDIA H100 SXM5NVIDIA A100 SXM4NVIDIA RTX PRO 6000NVIDIA L40SNVIDIA RTX 6000 ADAArm AGI CPU

Limitations

Pricing is not publicly listed; all plans require contacting sales. The platform is primarily EU-focused, limiting latency for non-European users. No free tier or pay-as-you-go for individuals. Managed inference only covers image (FLUX) and audio (Whisper) models—no managed text LLM endpoints. Limited global availability until H2 2026 expansion.

Integrations

NVIDIA GB300 NVL72NVIDIA B200 SXM6NVIDIA H200 SXM5NVIDIA H100 SXM5NVIDIA A100 SXM4NVIDIA RTX PRO 6000Arm AGI CPUInfiniBandNVLink v5RoCETerraformOpenTofuDockerKubernetesExpressVPNPython SDKVerda CLI

Hidden costs & gotchas

What the public pricing page doesn't put in bold. Captured from pricing-page footnotes, contract terms, and recurring complaints.

•Overages for serverless containers: pay-per-request beyond included quota (undisclosed rate)
•Bare-metal clusters require custom quote; minimum commitment likely
•Managed endpoints may incur usage fees beyond included credits

Where the pricing makes sense

The company stage and team size where DataCrunch's pricing actually pencils out — and where peers do it cheaper.

Verda's pricing is enterprise-oriented with no publicly listed tiers, so it fits teams that have a budget for custom quotes and need the latest hardware. It's not the cheapest—Lambda Labs and Vast.ai offer lower costs per GPU-hour with public pricing—but Verda's vertical integration and support may justify the premium for compliance-heavy or production workloads.

Setup time & first value

How long it actually takes to get something useful out of DataCrunch — broken out by persona, not the marketing-page minute.

For GPU instances: instant via web console or API (minutes). Instant Clusters: deploy 16x-128x GPUs in under 15 minutes. Bare-metal clusters: 1-3 business days with custom configuration. Serverless containers: deploy a containerized model in about 10 minutes using the Quickstart guide. Managed endpoints: select a model and get an API key instantly.

Switching to or from DataCrunch

How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.

Migrating in

→From Runpod: migrate containerized workloads with minor changes using serverless containers
→From AWS/GCP: use Terraform provider to recreate infrastructure; contact sales for dedicated migration support

Migrating out

↗To AWS: export data via object storage and recreate clusters using Terraform
↗To GCP: download VM images and upload to Compute Engine; use generic Terraform scripts

Recent material changes

Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.

•2026-06-02: Verda plans to deploy NVIDIA VR200 NVL72 and R200 in H2 2026 across Europe, US, and APAC.
•2026-04-30: Verda raises $117M to expand AI cloud infrastructure globally.
•2026-04-23: Verda completes SOC 2 Type II audit, enhancing security compliance.
•2025-11-05: DataCrunch rebrands to Verda with new focus on full-stack AI cloud.

Resources & Guides

Frequently Asked Questions

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CoreWeave

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Deci

Automated neural architecture search and optimization for NVIDIA-accelerated inference.

Tavily

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