DataCrunch: the address we list now redirects elsewhere · last checked 8 Nov 2026
DataCrunch
European GPU cloud with self-service GB300, B300 and B200 instances plus per-token open-weight inference
Blackwell silicon on a public price list, no capacity call required — that is still the reason to shortlist Verda. The per-token Inference preview makes it a two-headed product: rent GPUs or just call Kimi K3.1 Pro and DeepSeek V4.1 Flash through an OpenAI client. Weigh it against Lambda if EU residency matters; skip it if you want training pipelines handed to you.
Verdict generatedLast checked 35m ago · cite: rightaichoice.com/tools/datacrunch
- European AI startups that need self-service GB300, B300 or B200 capacity today rather than next quarter
- Inference teams that want Kimi K3.1 Pro or DeepSeek V4.1 Flash per token through an OpenAI client without owning GPUs
- Training teams needing InfiniBand or NVLink multi-node clusters scaling into the hundreds of GPUs
- Companies whose product depends on hardware-attested confidential computing for inference or fine-tuning
- Teams that need multi-region deployment outside the EU footprint Verda operates
- Buyers who want SageMaker- or Vertex AI-style managed pipelines instead of raw infrastructure
- Projects with no MLOps capacity to own drivers, cluster and storage plumbing
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Skip Verda if you want a fully managed ML platform with pre-built pipelines and models, or if your workloads must run across regions outside the EU.
Pay-as-you-go GPU hours from $0.2280/h (V100) to $12.05/h (GB300) suit a seed-stage team renting a single H100 at $4.426/h on-demand or $2.213/h spot, while reserved terms (8% off on-demand for a year, 25% for two) and custom quotes cover growth-stage and enterprise cluster buys. It undercuts hyperscaler list prices on comparable NVIDIA cards; check current Runpod and Lambda rates for your specific card.
Our "biggest gripe" note written Oct 21, 2026 quotes $10.69, $10.48, which no longer matches the verified price table, so it is being re-reviewed and is not shown.
In short
DataCrunch — European GPU cloud with self-service GB300, B300 and B200 instances plus per-token open-weight inference. Best for European AI startups that need self-service GB300, B300 or B200 capacity today rather than next quarter, Inference teams that want Kimi K3.1 Pro or DeepSeek V4.1 Flash per token through an OpenAI client without owning GPUs, Training teams needing InfiniBand or NVLink multi-node clusters scaling into the hundreds of GPUs. Plans from $0.048/hr.
No head-to-head comparison for DataCrunch yet — the alternatives below are the closest matches we list.
What is DataCrunch?
DataCrunch — European GPU cloud with self-service GB300, B300 and B200 instances plus per-token open-weight inference. Best for European AI startups that need self-service GB300, B300 or B200 capacity today rather than next quarter, Inference teams that want Kimi K3.1 Pro or DeepSeek V4.1 Flash per token through an OpenAI client without owning GPUs, Training teams needing InfiniBand or NVLink multi-node clusters scaling into the hundreds of GPUs. Plans from $0.048/hr.
Verda, formerly DataCrunch, rents NVIDIA GPU capacity out of its own EU data centers and now also sells frontier open-weight models per token through an OpenAI-compatible API. The on-demand catalog runs from a 1x Tesla V100 16GB at $0.2440/h to a 1x GB300 SXM6 288GB at $14.27/h, with B300, B200, H200, H100, A100, L40S, RTX PRO 6000 and RTX A6000 all bookable self-service. Spot pricing sits at roughly half of on-demand on nearly every SKU, so a B200 SXM6 180GB is $9.508/h on-demand against $4.754/h spot. The 2026 addition is Verda Inference, in preview: Kimi K3.1 Pro and DeepSeek V4.1 Flash served with 1M context, billed per 1M tokens at $2.00/$0.20/$10.00 for Kimi (input, cache read, output) and $0.15/$0.006/$0.60 for DeepSeek, with cached input pricing and an OpenAI client that works unchanged. Beyond raw rental: instant multi-node clusters over InfiniBand and NVLink with Slurm or Kubernetes on top, serverless containers that auto-scale to zero, NVMe block storage, a POSIX shared filesystem, an OCI container registry and S3-compatible object storage now in beta. Confidential-compute variants of B300, B200 and RTX PRO 6000 keep models encrypted in memory, in transit and at rest, and the Trust Center carries SOC 2 Type II plus the ISO 27001 family, C5 and GDPR compliance on 100% renewable power. Position it as infrastructure, not a managed pipeline: closer to Lambda or CoreWeave than to SageMaker. Buyers who want a single API for open-weight inference can skip the GPU layer entirely; teams that need to own the stack get the same platform underneath.
Key features
- On-demand NVIDIA GPU instances from 1x Tesla V100 16GB ($0.2440/h) to 1x GB300 SXM6 288GB ($14.27/h)
- Spot pricing at roughly half on-demand — B300 $6.205/h spot vs $12.41/h on-demand
- Verda Inference: Kimi K3.1 Pro and DeepSeek V4.1 Flash served per token through an OpenAI-compatible API
- 1M context windows on served open-weight frontier models, with cached input pricing
- Confidential computing on NVIDIA Blackwell (B300 CC, B200 CC, RTX PRO 6000 CC) with data encrypted in memory, in
- Confidential CPU instances on AMD EPYC
- GB300 NVL72 configurations from 1x tray to 2+ racks over NVLink v5
- Instant multi-node GPU clusters over InfiniBand, provisioned self-service
- 1x, 2x, 4x and 8x GPU instance shapes with NVLink variants
- NVLink, InfiniBand and RoCE networking
- Serverless GPU containers with auto-scaling and scale-to-zero billing
- Block storage (NVMe), POSIX shared filesystem, container registry and S3-compatible Object Storage (beta)
- Provisioning via console, REST API, SDK, Terraform or OpenTofu
- Reserved capacity discounts from 2% at one month to 25% at two years
- Slurm and Kubernetes orchestration; support for SkyPilot and dstack
Behind the verdict
The decision splits cleanly in two now. If you need GPUs, Verda sells them without a negotiation: GB300 at $14.27/h on-demand or $7.136/h spot, B300 at $12.41/$6.205, confidential B200 CC at $9.700/h. If you only need inference on open-weight models, you may never touch the GPU page at all — Verda Inference gives you Kimi K3.1 Pro and DeepSeek V4.1 Flash per token, through the OpenAI SDK, with cache-read pricing that punishes nobody for reusing a long prompt prefix. Where it bites: the footprint is EU-only. Multi-region deployments outside that map are not a thing here, and Object Storage is still beta, so do not make it your data lake this quarter. Reserved commitments top out at 25% off on-demand, reached only at a two-year term; if you are shopping for deep multi-year discounts, the published ladder stops there. In practice we'd reach for Verda when a training run needs InfiniBand multi-node capacity today and the alternative is waiting on a quota email. Instant clusters, the Terraform/OpenTofu providers and the SkyPilot and dstack workflow tooling mean an existing MLOps team can slot it in without rewriting their stack. Teams with no one to own drivers and cluster plumbing should look at a managed service instead. The confidential-compute line is the sharper differentiator than price. Hardware-attested inference and fine-tuning on B300 CC, B200 CC and RTX PRO 6000 CC is a narrow need, but for regulated EU workloads it is often the only box that ticks. Check that your framework path is supported before committing. Closest alternative: Lambda for the developer-experience comparison, CoreWeave for large committed clusters. Verda's edge is EU ownership plus the new inference API on the same bill. Start on spot for anything interruptible — the discount is real and
Models under the hood
as of 2026-11-08
Limitations
- Verda is an infrastructure and GPU cloud rather than an opinionated model platform: the published offering is GPU and CPU instances, clusters, storage, and serverless containers, not pre-trained foundation models or managed ML pipelines.
- Self-service hardware selection, auto-scaling containers, and multi-node InfiniBand clusters assume you bring your own training and inference workloads and own the surrounding MLOps.
- Verda Inference, which serves open-weight models through one API, is explicitly in preview.
- Confidential computing is restricted to NVIDIA Blackwell cards (B300, B200, RTX PRO 6000).
as of 2026-10-21
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Is DataCrunch still active?
DataCrunch: the address we list now redirects elsewhere · last checked 8 Nov 2026.
Viability score
How well maintained and how widely used is DataCrunch? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: November 2026
How we score →What do users say about DataCrunch?
What people actually say about DataCrunch — is it worth it?
Our community sentiment research written Jul 6, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about DataCrunch — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 14 Nov 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified DataCrunch 24 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed description, features, who it suits, summary, our verdict, who should skip it, pricing tiers
- — changed platforms, pricing page, limitations
- — changed who should skip it, pricing tiers, summary, features, integrations, who it suits, our verdict, description
- — changed limitations, pricing page
- — changed switching out, hidden costs, who it suits, who should skip it, setup time, pricing fit, limitations, our verdict, switching in, features, pricing tiers, description, use cases, summary
- — changed limitations
Showing the 6 most recent of 24 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does DataCrunch cost in 2026?
DataCrunch: Plans from $0.048/hr. Pricing last verified 14 Nov 2026.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where DataCrunch's pricing actually pencils out — and where peers do it cheaper.
Pay-as-you-go GPU hours from $0.2280/h (V100) to $12.05/h (GB300) suit a seed-stage team renting a single H100 at $4.426/h on-demand or $2.213/h spot, while reserved terms (8% off on-demand for a year, 25% for two) and custom quotes cover growth-stage and enterprise cluster buys. It undercuts hyperscaler list prices on comparable NVIDIA cards; check current Runpod and Lambda rates for your specific card.
Our hidden-costs note written Oct 21, 2026 quotes $10.69, $10.48, which no longer matches the verified price table, so it is being re-reviewed and is not shown.
Who should pick DataCrunch?
Pick DataCrunch if you are European AI startups that need self-service GB300, B300 or B200 capacity today rather than next quarter, Inference teams that want Kimi K3.1 Pro or DeepSeek V4.1 Flash per token through an OpenAI client without owning GPUs, Training teams needing InfiniBand or NVLink multi-node clusters scaling into the hundreds of GPUs, Companies whose product depends on hardware-attested confidential computing for inference or fine-tuning.
Look elsewhere if you are Teams that need multi-region deployment outside the EU footprint Verda operates, Buyers who want SageMaker- or Vertex AI-style managed pipelines instead of raw infrastructure, Projects with no MLOps capacity to own drivers, cluster and storage plumbing, Data-lake architects who need a production-grade object store today — Object Storage is still beta.
Skip it if Skip Verda if you want a fully managed ML platform with pre-built pipelines and models, or if your workloads must run across regions outside the EU.
GeneratedOur workflow scenarios written Oct 21, 2026 quotes $10.48, which no longer matches the verified price table, so it is being re-reviewed and is not shown.
Use cases
- Distributed training of large language models on multi-node InfiniBand clusters
- Serverless inference for real-time image and video generation with scale-to-zero billing
- Confidential AI workloads using hardware-attested multi-GPU compute
- Training sovereign or open-weight LLMs — Aleph Alpha's Kolibri was trained on Verda Blackwell clusters
- Cheap spot capacity for experimentation: H100 SXM5 80GB at $2.213/h versus $4.426/h on-demand
- Batch audio transcription or other batch jobs on auto-scaling GPU containers
- Prototyping on a single V100 or RTX A6000 before scaling to B300 clusters
- Running inference for generative video models — Verda co-engineered multi-GPU inference with 1X
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.
A single GPU instance is bookable from the console within minutes of signup. Terraform or OpenTofu users can have infrastructure-as-code provisioning running the same day. Multi-node InfiniBand clusters are self-service but realistically take a day or two including Slurm or Kubernetes setup and storage wiring. First value depends more on how much MLOps you already have than on Verda.
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.
- →From Runpod: containerized workloads move with minimal changes onto GPU instances or serverless containers
- →From a hyperscaler (AWS, GCP, Azure): re-provision GPU instances via Terraform or OpenTofu, move data to NVMe block storage or the shared filesystem, and repoint orchestration at Verda
- →From Slurm clusters: instant clusters support Slurm orchestration directly
- →From SkyPilot or dstack workflows: both are documented integrations, so existing launchers can target Verda
- ↗To a hyperscaler: export models and datasets from block storage and the shared filesystem, then rebuild cluster configs in the target's IaC format
- ↗To another GPU cloud: containerized inference workloads port cleanly via OCI registry images, but custom cluster and networking configs must be redone
- ↗To a managed ML platform: expect to rewrite training and inference glue as pipeline definitions
Integrations
Resources & guides
- Tutorialdatacrunch.io
Quick Deploy With Vllm
Step-by-step walkthrough from datacrunch.io
- Tutorialdatacrunch.io
Quick Migrate From Runpod
Step-by-step walkthrough from datacrunch.io
- Tutorialdatacrunch.io
Deploying Vllm Inference On Instant Cluster Using Ray
Step-by-step walkthrough from datacrunch.io
- Tutorialdatacrunch.io
Gang Scheduled Multi Node Training With Skypilot Kueue
Step-by-step walkthrough from datacrunch.io
- Tutorialdatacrunch.io
Deploying Nvidia Dynamo On A Kubernetes Instant Cluster
Step-by-step walkthrough from datacrunch.io
- Tutorialdatacrunch.io
In Depth Deploy With Sglang
Step-by-step walkthrough from datacrunch.io
- Tutorialdatacrunch.io
In Depth Deploy With Tgi
Step-by-step walkthrough from datacrunch.io
- Tutorialdatacrunch.io
In Depth Asynchronous Inference Requests With Whisper
Step-by-step walkthrough from datacrunch.io
- Tutorialdatacrunch.io
How To Publish Your First Docker Image To Docker Hub
Step-by-step walkthrough from datacrunch.io
- Tutorialdatacrunch.io
Deploy Deepseek R1 With Sglang Using Terraform Nvfp4 Inference
Step-by-step walkthrough from datacrunch.io
Tutorials & Learning
YouTube returned 6 videos for “DataCrunch”, and we withheld 5: 5 could not be judged, because “DataCrunch” is a single word that other videos use for other things. Showing the 1 we can prove is about DataCrunch.
Official links
What are the best DataCrunch alternatives?
The closest alternatives to DataCrunch we list are RunPod, Together AI and CoreWeave. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| RunPod | Paid | Pricing not published | — |
| Together AI | Freemium | Pricing not published | — |
| CoreWeave | Paid | Pricing not published | — |
Alternatives to DataCrunch
View all
RunPod
Rent GPU pods, Serverless inference endpoints and multi-node GPU clusters by the second on Runpod's AI developer cloud.
Together AI
Together AI is an AI cloud for open-weight models: per-token inference APIs with GPU clusters underneath.
Tools that pair well with DataCrunch
Common stack mates teams adopt alongside DataCrunch, with the specific reason each pairing earns its keep.
RunPod
Rent GPU pods, Serverless inference endpoints and multi-node GPU clusters by the second on Runpod's AI developer cloud.
Together AI
Together AI is an AI cloud for open-weight models: per-token inference APIs with GPU clusters underneath.
CoreWeave
CoreWeave is the AI-native GPU cloud for training, reinforcement learning post-training, and low-latency inference on NVIDIA cluster-scale
Best-of guides
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