First, the name. DataCrunch has rebranded to Verda, and datacrunch.io now resolves to verda.com. If you are searching for DataCrunch alternatives and finding a company you do not recognise, that is why. The product line carried over – same GPU instances, same European footprint, and a broader set of services than the old DataCrunch offered.
It is a strong platform, and worth saying so plainly before listing alternatives. Verda publishes an on-demand rate for its entire fleet including GB300 and B300, which most providers do not. Instant Clusters give self-service 16 to 64 GPU capacity over InfiniBand from the console, API, Terraform or SkyPilot with no sales call. Serverless containers scale to zero. Confidential computing variants offer hardware-attested inference and fine-tuning, which is genuinely rare. It is SOC 2 Type II certified and GDPR compliant, and spot pricing runs roughly 50% below on-demand across the board.
Teams still look elsewhere, usually for one of four reasons.
Storage is expensive. Every tier – NVMe, shared filesystem, container registry – is $0.20 per GiB per month. On a workload with a large dataset sitting alongside the compute, that adds up faster than the GPU rate suggests. Note the unit too: Verda bills per GiB, several competitors bill per GB, and 1 GiB is about 1.07 GB.
There is no low end and no AMD. The cheapest current card is an RTX A6000 at $0.61/hr, and below that only a Tesla V100. No consumer GPUs, and no AMD Instinct at all – if your stack runs on ROCm, Verda has nothing for you.
Short-term reserved discounts are thin. One month gets you 2% off, three months 3%, six months 4%. The meaningful discounts start at a year (8%) and two years (25%). If you want a mid-term commitment to move the needle, it does not.
Data transfer pricing is not published. The rate card covers compute and storage but not egress, so a data-heavy pipeline needs a conversation before you can model the bill.
Below are six alternatives. Every competitor figure was verified at source on 31 August 2026, and Runpod's own rates are pulled live from our pricing page.
What Verda publishes today
The baseline, verified 31 August 2026, on-demand per hour: GB300 $8.62, B300 $7.50, B200 $6.11, H200 SXM5 $4.00, H100 SXM5 $3.25, A100 SXM4 80GB $1.79, A100 SXM4 40GB $1.29, RTX PRO 6000 $1.89, L40S $1.37, RTX 6000 Ada $1.04, RTX A6000 $0.61, Tesla V100 $0.17. Spot is roughly half of each. Confidential computing variants carry a small premium – RTX PRO 6000 CC at $1.93, B200 CC at $6.23.
Serverless containers price separately and slightly higher: H100 $3.58/hr, H200 $4.40/hr, L40S $1.51/hr, with spot at about half. Storage is $0.20 per GiB per month across all three tiers.
The six best DataCrunch alternatives
1. Runpod
Best for: teams who need a card sized to the model, cheap storage alongside the compute, and inference that costs nothing between requests.
Runpod is an AI developer cloud with the same self-serve philosophy as Verda – published rates, no quota requests, live inventory in the console – but a wider range at the bottom and materially cheaper storage.
What is different:
- Storage costs a fraction as much. Network storage is $0.07/GB/month under 1TB and $0.05/GB/month over, against Verda's $0.20/GiB/month. Container disk is $0.10/GB/month. On a workload parking a large dataset next to the GPU, this is often a bigger line item than the hourly rate.
- A real low end. An RTX A5000 is {{gpu:rtx-a5000}}/hr, an L4 is {{gpu:l4}}/hr and an A40 is {{gpu:a40}}/hr on Secure Cloud. Community Cloud goes lower again – an RTX 4090 is {{gpu:rtx-4090:community}}/hr, and consumer cards are available in a way Verda simply does not offer.
- AMD is available. An MI300X with 192GB of VRAM is $2.39/hr on Secure Cloud, in configurations up to eight GPUs (verified 31 August 2026). Verda carries no AMD at all.
- Per-second billing across Pods, Serverless and Clusters, with no ingress or egress fees – published, not quoted on request.
- Serverless with sub-200ms cold starts via FlashBoot, scaling from zero to thousands of workers. Active workers remove cold starts entirely for steady traffic.
- 31 global regions, and SOC 2 Type II certified with HIPAA and GDPR compliance, reports, BAAs and DPAs available for security review.
Limitations: no confidential computing offering, which is a real Verda advantage if hardware attestation is a requirement. No in-house AI Lab. And if EU-only data residency is a hard constraint, Verda's European-first footprint is a cleaner story than a global network.
Pricing: H100 PCIe {{gpu:h100-pcie}}/hr, H100 SXM {{gpu:h100-sxm}}/hr, H200 {{gpu:h200}}/hr, B200 {{gpu:b200}}/hr, B300 {{gpu:b300}}/hr, A100 PCIe {{gpu:a100-pcie}}/hr, A100 SXM {{gpu:a100-sxm}}/hr, L40S {{gpu:l40s}}/hr, RTX 6000 Ada {{gpu:rtx-6000-ada}}/hr, RTX A6000 {{gpu:rtx-a6000}}/hr. Full pricing.
2. Crusoe
Best for: organisations where energy sourcing is part of the buying decision, and teams who want managed inference over a curated model catalogue.
Crusoe builds and operates its own data centers with an explicit focus on how the power is generated – the clearest differentiator of any provider here if you have sustainability commitments to report against. It also charges nothing for ingress or egress, which Verda does not publish, and it has built out managed AI services: Serverless Inference priced per million tokens, Serverless Fine-Tuning, and Self-Serve dedicated endpoints at $5.50/hr on H100 and $6.00/hr on H200.
Limitations: the newest hardware is Contact Sales rather than self-serve – GB200, B200 and MI355X all require a conversation, where Verda publishes rates for its entire fleet. The published range bottoms out at an L40S, so like Verda there is no genuine low end. Managed inference runs a curated model list rather than arbitrary containers.
Pricing (verified 31 August 2026): H200 $4.29, H100 $3.90, A100 80GB SXM $2.30, A100 80GB PCIe $2.00, L40S $1.50, AMD MI300X $3.45, per GPU-hour on demand. Storage is $0.06–$0.10 per GiB per month, which is well under Verda's.
3. DigitalOcean
Best for: AMD Instinct workloads, and teams who want managed databases and Kubernetes in the same account.
The obvious move if the gap you feel is AMD. DigitalOcean has the widest current Instinct range anywhere on this list – MI300X, MI325X, MI350X and MI355X – and the MI300X is self-serve at $2.59/GPU/hr. It also acquired Paperspace, bringing that GPU platform in-house, and unlike Verda it offers the surrounding services a full application needs.
Limitations: no serverless GPU tier, so no scale-to-zero – a step backwards from Verda's serverless containers. And a cost trap worth knowing: powered-off GPU Droplets are still billed, because the resources stay reserved. You must destroy the instance to stop charges.
Pricing (verified 31 August 2026, after a price change effective 1 August): H100 $4.41, H200 $4.47, L40S $1.57, RTX 6000 Ada $1.57, RTX 4000 Ada $0.76, AMD MI300X $2.59, MI325X $3.80, per GPU per hour on demand. Billed per second with a five-minute minimum.
4. Lambda
Best for: teams who want a maintained ML software stack, or physical hardware from the same vendor.
Lambda Stack – its maintained distribution of CUDA, cuDNN, PyTorch and TensorFlow – is the genuine draw. If you have lost a day to a driver mismatch, that is worth real money. Lambda also sells workstations and servers outright, which nobody else here does.
Limitations: this is a step down from Verda on almost every structural dimension. Five GPU models against Verda's twelve. No serverless tier at all, so no scale-to-zero. Clusters start at 16 GPUs on a two-week minimum arranged through sales, where Verda's Instant Clusters are self-service. Nothing below $0.79/hr. Rates are quoted plus tax, which is unusual in this set.
Pricing (verified 31 August 2026): B200 $6.69, H100 SXM $3.99, A100 SXM 80GB $2.79, A100 40GB $1.99, V100 $0.79, per GPU per hour, plus tax.
5. Vultr
Best for: geographic spread beyond Europe, and teams who can commit to a contract term.
If Verda's European centre of gravity is the constraint – you need inference close to users in North America, APAC or emerging markets – Vultr's 20-plus locations are the argument. It offers both cloud GPU instances and bare metal, with a clean console and solid API, and carries AMD Instinct alongside NVIDIA.
Limitations: read the rates carefully, because the attractive ones are not on-demand. The H100 figure is 24-month contract pricing, the B200 figure is a 48-month contract, and the AMD figures are preemptible instances that can be reclaimed. True on-demand rates run well above Verda's. No serverless tier.
Pricing (verified 31 August 2026): genuine on-demand is A100 PCIe 80GB $2.397/hr, L40S $1.671/GPU/hr, A40 $1.712/hr, GH200 $1.990/GPU/hr. The H100 at $1.990/GPU/hr requires a 24-month commitment; 36-month prepaid brings A100 PCIe to $1.290/GPU/hr.
6. The hyperscalers: AWS, Google Cloud and Azure
Best for: teams who need databases, identity and analytics in the same account as the GPUs.
Grouped, because the reason to pick any of them over Verda is the same: you need a full cloud rather than a better GPU deal. AWS has the widest instance catalogue plus Trainium and Inferentia. Google Cloud has TPUs, which are genuinely differentiated silicon. Azure is the answer when the organisation already lives in Entra and Microsoft 365.
Limitations: more operationally complex than Verda, all three charge for egress, and current-generation GPU capacity frequently requires a quota request rather than a console click. None will be cheaper per GPU-hour.
Pricing: none publishes a flat, comparable GPU rate card. AWS and Azure route through calculators; Google Cloud's rates vary by region and machine type. We could not verify comparable published hourly figures on 31 August 2026, so we are not quoting any. See our AWS and Azure alternatives pages.
How they compare
All competitor figures verified 31 August 2026. Runpod figures are live. Mind the storage units – Verda and Crusoe bill per GiB, Runpod per GB, and 1 GiB is roughly 1.07 GB.
How to choose
Stay on Verda if you need EU data residency, confidential computing, or self-service InfiniBand clusters. Those three together are a combination nobody else on this list matches, and the published-rates-on-everything policy is genuinely unusual.
Move to Runpod if the friction is storage cost, the missing low end, or the lack of AMD. If most of your models fit in 24GB, or you are parking a large dataset next to the compute, those two lines are where the money is.
Move to Crusoe if energy sourcing matters to your organisation, or you want managed inference over a curated model list rather than running your own containers.
Move to DigitalOcean if you need AMD Instinct, particularly the newer MI325X and MI355X.
FAQ
Is DataCrunch the same company as Verda?
Yes. DataCrunch rebranded to Verda, and datacrunch.io now resolves to verda.com. The GPU instance line carried over, alongside newer products including Instant Clusters, serverless containers and confidential computing. If you have old bookmarks or documentation referencing datacrunch.io, they should redirect.
Why is Verda's storage so much more expensive than its compute?
Verda charges $0.20 per GiB per month uniformly across NVMe, shared filesystem and container registry – it is high-performance NVMe at 100k IOPS rather than commodity storage, which explains part of it. The practical consequence is that storage-heavy workloads shift the economics: a competitive GPU rate can be offset by a dataset sitting on disk between runs. Worth modelling total monthly cost rather than comparing hourly rates alone.
Does Verda offer AMD GPUs?
No. The fleet is entirely NVIDIA, from Tesla V100 through GB300. If your workload runs on ROCm, the options on this list are DigitalOcean, which carries MI300X through MI355X, Runpod at $2.39/hr for the MI300X, Crusoe at $3.45/hr, and Vultr on preemptible instances.
What is confidential computing and does anyone else offer it?
It runs your workload inside a hardware-attested trusted execution environment, so the infrastructure operator cannot inspect the data or model in memory. Verda offers it on RTX PRO 6000, B200 and B300 at a small premium over the standard rate. It is a genuine differentiator – none of the other providers on this list offer an equivalent self-serve product, so if it is a hard requirement, that alone may keep you where you are.
How do the spot and reserved discounts compare?
Verda's spot pricing is roughly 50% off on-demand across the fleet, which is competitive and applies broadly. The reserved side is weaker at short terms: 2% at one month, 3% at three, 4% at six, then 8% at a year and 25% at two years. Vultr's prepaid discounts run deeper but require 24 to 48 month commitments, and DigitalOcean offers 12-month reserved plus a spot tier. If you want a discount without a long commitment, spot is the more useful lever everywhere.
Get started
Runpod offers on-demand GPUs with no minimum spend and no quota request, billed by the second, with storage from $0.05/GB/month and no egress fees. See current pricing, or deploy a Pod to test your own workload.
