Crusoe occupies an unusual position among GPU providers. It builds and operates its own data centers with an explicit focus on energy sourcing, which makes it a serious option for organisations with sustainability commitments to answer for, and it has assembled a genuinely current fleet – GB200 NVL72, B200 and AMD MI355X at the top end – alongside managed inference and serverless fine-tuning.
Teams still look elsewhere, usually for one of three reasons.
Much of the fleet is not self-serve. GB200, B200 and MI355X are all Contact Sales rather than a published rate. If you want to price a Blackwell training run without booking a call, you cannot.
There is no low end. The published fleet bottoms out at an L40S at $1.50/GPU-hr. There are no consumer cards and no small data center GPUs, so a model that fits comfortably in 24GB has nothing appropriate to rent – you pay for a 48GB card or more.
The managed AI services are model-catalogue shaped. Serverless Inference and Serverless Fine-Tuning are strong products, but they run a curated model list. If you want to deploy your own container as an autoscaling endpoint, that is a different primitive.
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, so they cannot go stale.
What Crusoe publishes today
For reference, since the comparison only works if you know the baseline. Verified 31 August 2026, on-demand, per GPU-hour: H200 $4.29, H100 $3.90, A100 80GB SXM $2.30, A100 80GB PCIe $2.00, L40S $1.50, AMD MI300X $3.45. GB200, B200 and MI355X are Contact Sales, and spot rates are quoted on request rather than published.
Storage runs $0.06 to $0.10 per GiB per month depending on type, managed Kubernetes is $0.10 per cluster hour, and Crusoe charges nothing for ingress or egress – which is worth crediting, since most providers do.
The six best Crusoe alternatives
1. Runpod
Best for: teams who want the whole fleet self-serve, a real low end, and inference that scales to zero.
Runpod is an AI developer cloud built around the idea that you should be able to see the price and deploy without a conversation. Where Crusoe gates its newest hardware behind sales, Runpod publishes a rate for everything it sells and shows live inventory in the console at deployment time.
What is different:
- The low end actually exists. 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 still – an RTX 4090 is {{gpu:rtx-4090:community}}/hr. For workloads that do not need an 80GB card, this is the difference between a sensible bill and an absurd one.
- Serverless for your own containers. Endpoints scale from zero to thousands of workers on request volume, with sub-200ms cold starts via FlashBoot. You bring the image; there is no curated model list to fit into.
- Per-second billing across Pods, Serverless and Clusters, with no ingress or egress fees.
- Blackwell without a sales call. B200 is {{gpu:b200}}/hr and B300 is {{gpu:b300}}/hr, both self-serve.
- 31 global regions, no quota requests.
- SOC 2 Type II certified, HIPAA and GDPR compliant, with reports, BAAs and DPAs available for security review.
Limitations: Runpod does not build its own data centers, so if vertically integrated power sourcing is the reason you chose Crusoe, that is not something we replicate. There are no managed databases or ancillary cloud services. And we do not offer a curated managed-inference model catalogue in the way Crusoe does – Public Endpoints cover popular models, but the primary pattern here is deploying your own.
Pricing: H100 PCIe {{gpu:h100-pcie}}/hr, H100 SXM {{gpu:h100-sxm}}/hr, H200 {{gpu:h200}}/hr, A100 PCIe {{gpu:a100-pcie}}/hr, A100 SXM {{gpu:a100-sxm}}/hr, L40S {{gpu:l40s}}/hr. AMD MI300X is $2.39/hr on Secure Cloud (verified 31 August 2026). Storage starts at $0.05/GB/month. Full pricing.
2. Verda (formerly DataCrunch)
Best for: European data residency, confidential computing, and self-service multi-node clusters.
Verda rebranded from DataCrunch, moving from datacrunch.io to verda.com. It is the closest structural match to Crusoe on this list – a European provider with its own AI Lab, a current fleet running up to GB300 NVL72, and an emphasis on doing the whole lifecycle rather than just renting instances.
Where it differs from Crusoe is self-service. Instant Clusters give you 16 to 64 GPUs over InfiniBand from the console, API, Terraform or SkyPilot with no sales engagement, and the entire fleet including GB300 and B300 has a published on-demand rate. 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 and GDPR compliant, and spot pricing runs roughly 50% below on-demand across the board.
Limitations: storage is $0.20 per GiB per month across every tier, which is expensive next to most of this list – and mind the unit, because GiB and GB are not the same thing. Reserved discounts are thin at short commitments: 2% at one month, 3% at three, 4% at six, reaching 8% at a year and 25% at two. No consumer GPUs and no AMD.
Pricing (verified 31 August 2026): GB300 $8.62, B300 $7.50, B200 $6.11, H200 SXM5 $4.00, H100 SXM5 $3.25, A100 SXM4 80GB $1.79, A100 40GB $1.29, RTX PRO 6000 $1.89, L40S $1.37, RTX 6000 Ada $1.04, RTX A6000 $0.61, Tesla V100 $0.17, per hour on demand.
3. Lambda
Best for: teams who want a maintained ML software stack and physical hardware from the same vendor.
Lambda's real differentiator is Lambda Stack, its maintained distribution of CUDA, cuDNN, PyTorch and TensorFlow. If you have lost a day to a driver mismatch, that has value. It also sells workstations and servers outright, which nobody else here does.
Limitations: the fleet is narrow – five GPU models, nothing below $0.79/hr, no consumer cards. There is no serverless tier at all, so no scale-to-zero and no per-request billing. Clusters start at 16 GPUs on a two-week minimum through sales rather than self-serve, so on that dimension it is more restrictive than Crusoe, not less. Instance access is described in Lambda's own wording as first-come.
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 – which is unusual in this set and worth factoring in.
4. DigitalOcean
Best for: the widest current AMD Instinct range, and a platform you can learn in an afternoon.
If you are on Crusoe for the MI300X, DigitalOcean is the provider with the deepest AMD bench: MI300X, MI325X, MI350X and MI355X are all available, and the MI300X is self-serve at $2.59/GPU/hr where Crusoe's is $3.45. It also acquired Paperspace, bringing that GPU platform and its tooling in-house, and it offers managed databases and Kubernetes alongside – more of a general cloud than Crusoe is.
Limitations: no serverless GPU tier, so no scale-to-zero. And a real cost trap worth knowing before you start: powered-off GPU Droplets are still billed, because the underlying resources stay reserved. You have to 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.
5. Vultr
Best for: geographic spread, and teams who can commit to a contract term.
Vultr runs more than 20 locations worldwide with both cloud GPU instances and bare metal. Its Blackwell and Grace Hopper capacity is current for a provider its size, and it carries AMD Instinct alongside NVIDIA. If your constraint is putting inference close to users in many regions, its footprint is the argument.
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 are considerably higher. 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 needs 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 together because the reason to choose any of them over Crusoe is the same: you need a full cloud, not a better GPU deal. AWS has the widest instance catalogue plus Trainium and Inferentia; Google Cloud has TPUs, which are genuinely differentiated hardware; Azure is the answer if your organisation already lives in Entra and Microsoft 365.
Limitations: all three are more operationally complex than Crusoe, all three charge for egress where Crusoe does not, and GPU capacity in a given region frequently requires a quota request. None of them will be cheaper per GPU-hour.
Pricing: none of the three publishes a flat, comparable GPU rate card – AWS and Azure route through calculators, and 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 for detail.
How they compare
| Provider | Blackwell self-serve? | Cheapest published GPU | Scale to zero | Egress |
|---|---|---|---|---|
| Crusoe | No, Contact Sales | $1.50 L40S | Managed inference and fine-tuning only | None |
| Runpod | Yes, B200 and B300 | {{gpu:rtx-a5000}} Secure, lower on Community | Yes, own containers | None |
| Verda | Yes, up to GB300 | $0.17 V100 | Yes, own containers | Not published |
| Lambda | Yes, B200 | $0.79 V100, plus tax | No | Not published |
| DigitalOcean | Yes, B300 and AMD MI355X | $0.76 RTX 4000 Ada | No | Included allowance |
| Vultr | Contract terms | $1.671 L40S on demand | No | Included allowance |
| AWS / GCP / Azure | Varies, quota dependent | Not published as flat rates | Yes, via managed services | Charged |
All competitor figures verified 31 August 2026. Runpod figures are live.
How to choose
Stay on Crusoe if energy sourcing and vertically integrated data centers are part of what you are buying, or if their managed inference catalogue covers the models you serve. Those are real assets and nothing on this list replicates the first one.
Move to Verda if you want the same full-lifecycle shape but need European residency, confidential computing, or self-service multi-node clusters without a sales conversation.
Move to Runpod if the friction is the low end and the sales gate – you want a card sized to the model rather than the smallest thing available, published rates on the whole fleet, and inference endpoints that cost nothing between requests.
Move to DigitalOcean if you are there for AMD specifically and want a wider Instinct range at a lower MI300X rate.
FAQ
Why does Crusoe not publish prices for its newest GPUs?
GB200, B200 and MI355X are all listed as Contact Sales rather than a rate. That is common for scarce, high-demand hardware – capacity gets allocated through commercial conversations rather than a console button. It is not unusual, but it does mean you cannot budget a Blackwell run from the public page, which is the practical reason teams look at providers that publish across the whole fleet.
Does Crusoe have a serverless product?
Yes, though it is a different shape from a generic serverless GPU tier. Crusoe offers Serverless Inference over a curated catalogue of open models priced per million tokens, Serverless Fine-Tuning priced per million training tokens, and Self-Serve Deployments with dedicated hourly endpoints at $5.50/hr on H100 and $6.00/hr on H200. What it does not offer is deploying an arbitrary container as an autoscaling, scale-to-zero endpoint – for that, Runpod and Verda are the closest options here.
Which alternative is cheapest for small models?
Crusoe's floor is an L40S at $1.50/GPU-hr, so anything below 48GB of VRAM is where alternatives open a gap. Verda's V100 at $0.17/hr is the cheapest verified figure in this set, DigitalOcean's RTX 4000 Ada at $0.76 is the cheapest current-generation option, and Runpod's Community Cloud rates sit below both for consumer cards. If most of your workloads fit in 24GB, this is the largest single saving available.
Do any of these charge for data transfer?
Crusoe and Runpod both charge nothing for ingress or egress, which is the exception rather than the rule. DigitalOcean and Vultr bundle a transfer allowance with each instance. Lambda and Verda do not publish transfer pricing on their rate cards, so ask before you plan a data-heavy pipeline. All three hyperscalers charge for egress.
Is switching from Crusoe difficult?
For containerised training and inference, generally not – every provider here takes standard Docker images, and Crusoe does not lock you into a proprietary runtime. What does not move cleanly is anything built on Crusoe's managed services: models served through Serverless Inference or tuned through Serverless Fine-Tuning need redeploying as your own containers elsewhere. Data movement is free on Crusoe's side, which helps.
Get started
Runpod offers on-demand GPUs with no minimum spend and no quota request, billed by the second, with published rates across the whole fleet. See current pricing, or deploy a Pod to test your own workload.
