The right Voltage Park alternative depends on what you're running and how long you need the hardware. AI teams after smaller or differently shaped GPU capacity can pick AMD-based clouds, bare metal servers, a sovereign cloud with UK data centres or hourly rental, but the GPU maker matters as much as the provider you choose.
Do you need a different GPU, a different contract or a different kind of provider? Voltage Park offers on-demand and reserved NVIDIA GPU cloud infrastructure, and teams look elsewhere for all three reasons. Some want AMD hardware. Some want bare metal, or a cloud that keeps data in the UK. And some just want to rent a GPU by the hour for a week of experiments without a long reservation.
Below are nine alternatives, in no particular order. None is a hyperscaler, and most are small, independent providers. This list sticks to datacentre GPU clouds rather than networks that pool other people's idle hardware. For those pooled networks, compare distributed GPU network alternatives to Salad. The hardware question comes first, because it's the one most likely to derail a move.
AMD or NVIDIA: check the chip before the contract
Two of the nine, Hot Aisle and TensorWave, are built on AMD Instinct GPUs. Crusoe lists both NVIDIA and AMD hardware alongside its managed inference and fine-tuning products. Every other cloud here leads with NVIDIA.
That's more than a spec sheet detail. A great deal of AI code is written for NVIDIA's CUDA, and AMD's software stack is ROCm. If your training scripts, custom kernels or container images were built on CUDA, give them a test run on ROCm before you commit a long training job to AMD hardware. A day spent porting and checking beats discovering a missing library halfway through a run you can't easily restart.
The second question is the shape of the capacity: on-demand or reserved. A cloud with a long reservation queue is no use for a one-week experiment, and hourly rental may not be what you want for months of steady training. Ask how quickly you can get GPUs today, not just which GPUs exist on the website.
Nine datacentre GPU clouds in one table
| Tool | What it offers | Good for |
|---|---|---|
| Civo | Sovereign cloud with GPU options | UK data centres for public, private and AI work |
| Crusoe | AI cloud, NVIDIA and AMD GPUs | Choosing NVIDIA or AMD, plus managed inference |
| Cudo Compute | Enterprise GPU cluster builder | Having a production GPU cluster designed and run for you |
| Denvr Dataworks | GPU cloud, on-demand or dedicated | Flexible on-demand or dedicated GPU access |
| Hot Aisle | AMD GPU cloud for inference | Isolated AMD GPU compute for production inference |
| Latitude.sh | Bare metal GPU cloud | Bare metal GPU servers beside Kubernetes and VMs |
| Massed Compute | On-demand NVIDIA GPU rental | Hourly NVIDIA GPUs with CUDA ready to go |
| Oblivus | On-demand GPU cloud | Spinning up scalable GPU compute on demand |
| TensorWave | AMD Instinct GPU cloud | Dedicated AMD Instinct capacity for scaling models |
Sorted by the work you run
Short experiments and hourly work. Massed Compute rents NVIDIA GPUs by the hour with CUDA already installed, which takes a step out of getting started. Oblivus offers scalable GPU compute on demand for smaller AI projects.
Training or inference with a choice of commitment. Denvr Dataworks lets you pick on-demand or dedicated access, useful when a project starts as a test and turns into steady production.
Bare metal and a full platform. Latitude.sh puts bare metal GPU servers next to Kubernetes, virtual machines, storage and managed databases, so the GPU box isn't sitting on its own island.
Data that stays in the UK. Civo positions itself as a sovereign cloud with UK data centres, covering public, private and AI workloads. That's worth a look for UK teams with data residency requirements, though what counts as compliant varies, so check with your regulator or adviser.
Big builds done for you. Cudo Compute designs, commissions and operates production GPU clusters, which suits organisations planning large training or inference capacity rather than renting a few machines.
The AMD clouds and the mixed NVIDIA and AMD option are covered in the section above.
Each GPU cloud in a line
In alphabetical order.
Civo
Best for: Organisations wanting a sovereign cloud and AI platform with UK data centres, covering public, private and AI workloads in one place.
Crusoe
Best for: AI teams who want both NVIDIA and AMD GPUs on offer, plus managed inference and fine-tuning products, from a cloud built around an energy-first approach.
Cudo Compute
Best for: Larger organisations that want production AI infrastructure and NVIDIA GPU clusters designed, commissioned and operated for them, which the vendor says includes securing land and power.
Denvr Dataworks
Best for: Developers and AI operators who want the choice of on-demand or dedicated access to current GPU architectures, for either training or inference.
Hot Aisle
Best for: Inference teams happy on AMD Instinct GPUs who want isolated compute provisioned by default, from a developer-first cloud.
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Latitude.sh
Best for: Engineers who want bare metal GPU servers on the same platform as Kubernetes, virtual machines, storage and managed databases.
Massed Compute
Best for: Short experiments on NVIDIA hardware, rented by the hour with CUDA pre-installed; the vendor's own pitch is no contracts and no upsells.
Oblivus
Best for: Smaller AI projects needing scalable GPU compute on demand, from a cloud whose own FAQ says it doesn't run traditional free trials.
TensorWave
Best for: Model builders committed to AMD Instinct accelerators who want dedicated cloud compute; the vendor pitches it as high-performance capacity for scaling models.
My advice before you move a training job
Start with the workload, not the provider. Write down whether it's training or inference, how long it'll run, which framework and drivers it needs, and whether the data has to stay in a particular country. Those answers knock out most of this list on their own, and they stop a sales call steering you towards hardware you don't need.
If you're not sure a job needs a GPU at all, my explainer on CPU vs GPU covers when graphics cards earn their keep. And if you orchestrate everything in containers, my list of Kubernetes alternatives is worth reading alongside this one, because the GPU cloud is only half the stack.
Then run something small and real on your top two before signing anything long. A short job that loads your data, trains for an hour and saves a checkpoint will tell you more about drivers, storage speed and support than any benchmark page.
Questions to put to every GPU provider
- Which GPU maker and models can I get today, and is my framework supported on them?
- Is capacity on-demand, reserved or both, and how long is the wait for each?
- Do I get bare metal or a virtual machine, and can I install my own drivers?
- Where are the data centres, and who can access my data and model weights?
- How do storage and moving large datasets in and out work?
- What happens at the end of a reservation if my job is still running?
- What support do I get when a node fails in the middle of the night?
Do you make GPU cloud services? Tool makers can ask to be considered for a featured place on this list.
Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.
Frequently asked questions
Can I run CUDA code on AMD GPUs?
Not always as it stands. AMD's software stack is ROCm, and code written for NVIDIA's CUDA needs testing, and sometimes changes, before it runs there. Try a short job first.
What's the difference between bare metal and a virtual GPU server?
Bare metal gives you the whole physical machine with no virtualisation layer in between. A virtual server shares the underlying hardware, which is quicker to start but gives you less control.
Should I choose on-demand or reserved GPUs?
On-demand suits experiments and bursts of work you can't predict. Reserved capacity suits long, steady training where you know you'll need the hardware for weeks or months.
What does a sovereign cloud mean?
It usually means a cloud that keeps data and operations within one country. If your data has residency rules, check exactly where it's stored and who can access it, and confirm the requirements with your regulator.
Do I need a GPU cloud at all?
Not for every job. Training and serving larger AI models usually needs GPUs, but many data and web tasks run happily on ordinary CPU servers.
How do I test a GPU cloud before committing?
Run a short, real job: load your data, train or serve for an hour, save a checkpoint and restore it. Note how long setup took and how support responded.
