The main Fluidstack alternatives are large purpose-built AI clouds for reserved capacity, specialist clouds built on AMD accelerators, and on-demand GPU hosts you can start yourself. The split comes down to one question: do you need a data centre's worth of capacity built for you, or a handful of GPUs by Friday?
Quick picks by what you need
- A huge reserved cluster for a long training run: look at the large AI clouds.
- NVIDIA and AMD under one roof, plus managed inference: look for a large cloud that lists both makers.
- A stack built on AMD's ROCm software: look at the AMD-only cloud.
- GPUs by the hour, with clusters later: look at the on-demand GPU clouds.
- Capacity in Europe: check which providers name European data centres on their own pages.
All eight clouds below sit in that guide somewhere. They're listed in no particular order, alphabetically, and every description comes from the vendor's own website, so treat each claim as theirs.
Why scale is the first question
If you've been weighing up a capacity builder, you've seen what that kind of home page sells: gigawatt data centres, built in America and delivered in months. It talks about building capacity and hiring, not about an instance you click to start. That's perfect for a lab that needs a whole site of GPUs. It's no help at all if you need eight GPUs for a fine-tuning job next week.
The alternatives split the same way. Some lead with data centres and capacity deals, and you'll talk to a sales team before you see a machine. Others lead with on-demand GPUs you launch from a dashboard or an API. Work out which side you're on before you book a single call, and you'll cut this list in half straight away.
If you're not sure your workload needs GPUs at all, my explainer on CPU vs GPU walks through the difference in plain English.
Eight AI clouds side by side
| Tool | Type | Good for |
|---|---|---|
| CoreWeave | Large AI cloud | Large training runs on purpose-built AI data centres |
| Crusoe | Large AI cloud | NVIDIA and AMD GPUs with managed inference on top |
| Hyperstack | GPU cloud | A wide NVIDIA menu, including spot VMs |
| Massed Compute | GPU cloud | Hourly GPUs with CUDA ready, plus InfiniBand clusters |
| Nebius | Large AI cloud | Training through inference on one AI cloud |
| Nscale | Large AI cloud | Capacity in Norway, the UK and other named sites |
| TensorWave | AMD cloud | Workloads built on AMD Instinct accelerators |
| Voltage Park | GPU cloud | On-demand now, reserved later, same NVIDIA hardware |
The eight clouds, A to Z
CoreWeave
Best for: Labs and AI companies that want purpose-built AI data centres, with serverless fine-tuning and managed reinforcement learning on the same platform.
Crusoe
Best for: Workloads that need both NVIDIA and AMD GPUs, from H100 up to GB300 and MI355X, with managed inference and fine-tuning alongside.
Hyperstack
Best for: Engineers who want to choose from H100 SXM through to GB300 NVL72, with spot VMs for jobs that can restart.
Massed Compute
Best for: Developers who want an hourly NVIDIA GPU with CUDA pre-installed and a route to InfiniBand clusters through an API later.
Nebius
Best for: Groups who'd like one AI cloud covering the whole path from training a model to serving it.
Nscale
Best for: Buyers who want capacity in Europe, with data centres in Norway, the UK, Portugal and Iceland named alongside the US.
TensorWave
Best for: AMD shops whose code already runs on ROCm and want a cloud built only on MI355X and MI325X accelerators.
Voltage Park
Best for: Researchers who want to rent NVIDIA H100, B200 or GB300 on demand today and reserve once usage settles.
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NVIDIA, AMD and why it matters
Most AI code is written for NVIDIA's CUDA software, which is why almost every cloud here leads with NVIDIA hardware. AMD's accelerators run on a different software stack called ROCm. Popular frameworks support it, but custom kernels and older libraries sometimes don't, so moving a mature CUDA codebase isn't a weekend job.
That doesn't make AMD a bad choice. If your team has already tested your models on ROCm, an AMD-first cloud can be a smart way to get capacity. Just run your real training script on it before you sign anything long term.
Questions to ask before you sign
- Can I start on demand and reserve later in the same account? Some clouds treat these as separate products with separate contracts.
- Which GPUs can I launch this week? A model on a spec sheet isn't a machine you can start today.
- Where are the data centres? If your customers care about data location, get it in writing.
- What's the networking between nodes? Multi-node training needs fast interconnects such as InfiniBand.
- What happens when a node fails mid-run? Ask about support on the plan you'll be on, not the top tier.
For the parts of your stack that don't need a GPU at all, my list of cloud server hosting covers general-purpose servers.
My advice on picking an AI cloud
Size the job before you shortlist anyone. If you need a reserved cluster for months, go straight to the large AI clouds and ask for delivery dates in writing. If you need GPUs this week, start on demand and only reserve once your usage is steady. Either way, run one real job on two providers before you commit, because a week of logs tells you more than any spec sheet.
Do you make AI cloud or GPU infrastructure software? 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
What is a neocloud?
It's an informal name for the newer clouds built mainly to rent GPUs for AI, as opposed to general-purpose clouds that sell everything from storage to email.
Should I pick NVIDIA or AMD GPUs?
Most AI code is written for NVIDIA's CUDA, so NVIDIA is the safer default. AMD can work well if your framework supports ROCm, but test your own code first.
What are spot VMs?
They're spare GPU machines that can be taken back at short notice. They suit jobs that save progress and can restart, not live services.
Do I need InfiniBand?
Only if you train across many machines at once. It speeds up communication between nodes. A single-machine job doesn't need it.
How long does it take to get a reserved cluster?
It varies by provider and hardware. Ask for a delivery date in writing, and keep an on-demand option ready in case it slips.
