Choose a replacement by how you want to rent GPUs, not by the biggest brand. On-demand clouds suit short training and inference jobs, AMD-based clouds suit teams ready to move off NVIDIA, regional providers suit data rules, and developer platforms suit teams who want the workflow handled too.
Renting a GPU should be the easy part of an AI project. For developers, researchers and small AI teams looking past Massed Compute, it still can be, as long as the provider you pick still wants a small team's business. That's the first filter, ahead of chip type or speed, and it's why this list of nine GPU clouds, in no particular order, starts with a shift most people haven't noticed.
Why some GPU clouds are chasing bigger customers
Here's what's changed. Several GPU providers that used to rent by the hour now describe themselves very differently. CUDO Compute's site talks about securing land and power for enterprise AI. Fluidstack talks about gigawatt data centres. Ori now sits under Radiant's AI factory pitch.
That's a sensible business move for them, because large customers sign long contracts. For a small team, though, it can mean slower replies, minimum commitments or a sales call before you see a single instance. So before you shortlist any provider here, confirm it still sells a single on-demand GPU to a team your size, and time how long your first instance takes to start on your own workload.
Nine GPU clouds side by side
| Tool | Type of cloud | Good for |
|---|---|---|
| Crusoe | Energy-first AI cloud | Teams wanting fine-tuning and deployment services too |
| Denvr | Sovereign GPU cloud | Workloads that need to stay on Canadian infrastructure |
| Hot Aisle | Developer-first AMD cloud | Developers who prefer a terminal, API or CLI |
| io.net | GPU and open model access | Pairing rented GPUs with open-source models |
| Jarvislabs | On-demand NVIDIA GPU cloud | Small teams training and serving LLMs |
| Latitude.sh | Global bare metal cloud | AI-native companies deploying across regions |
| Lightning AI | Build, train and deploy cloud | PyTorch teams who want build, train and deploy together |
| TensorWave | AMD Instinct cloud | Teams moving to AMD accelerators and ROCm |
| Voltage Park | On-demand and reserved GPU cloud | Mixing short jobs with reserved NVIDIA capacity |
How you'd rent from each one
These providers differ less in what they rent and more in how they expect you to rent it.
On-demand GPU clouds
Jarvislabs, Voltage Park and io.net are the closest in feel to renting by the job. You pick a GPU, start an instance and stop it when the run finishes. One of them also lets you reserve capacity for longer projects, and another pairs GPUs with open-source models.
AMD-based clouds
TensorWave and Hot Aisle are built on AMD accelerators rather than NVIDIA. That's a real option now that ROCm support has grown, but check that your framework and libraries run cleanly on AMD before you move a production workload.
Sovereign and regional providers
Denvr leads with compute that stays on Canadian infrastructure, which matters if your data rules or your customers ask where processing happens. Latitude.sh goes the other way, with bare metal and GPU metal in several regions for teams that want to deploy close to users.
Platforms with a developer workflow
Lightning AI wraps GPUs in a place to build, train and deploy, so you spend less time on setup scripts. Crusoe adds fine-tuning and deployment services on top of its own infrastructure. Both suit teams who'd rather not assemble every piece themselves.
All nine providers, A to Z
Alphabetical and unranked. Every line reflects how the provider describes itself.
Crusoe
Best for: Teams who want AI infrastructure built on what the vendor calls an energy-first approach, with fine-tuning and deployment services and round-the-clock support.
Denvr
Best for: Organisations that care where compute sits: on-demand or reserved GPUs across bare metal, VMs and containers, which the vendor describes as sovereign Canadian infrastructure.
Hot Aisle
Best for: Developers who live in the terminal and want isolated AMD GPU compute launched from a terminal UI, an API or a command line.
io.net
Best for: Builders who want GPU access and open-source models from one platform, which the vendor pitches against the major clouds.
Jarvislabs
Best for: Small teams renting NVIDIA GPUs on demand for LLM training and inference, in what the vendor calls one platform from a GPU to an endpoint.
Latitude.sh
Best for: AI-native companies that want bare metal, GPU metal and Kubernetes across a global footprint, with the vendor's pitch being easy global deployment and scaling.
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Lightning AI
Best for: PyTorch developers building, training and deploying models with on-demand GPUs and clusters in one place, which the vendor calls the AI cloud PyTorch developers love.
TensorWave
Best for: Anyone ready to run on AMD Instinct accelerators and ROCm in dedicated cloud compute, which the vendor says avoids vendor lock-in.
Voltage Park
Best for: Researchers mixing on-demand runs with reserved NVIDIA capacity across several data centres, which the vendor describes as secure and scalable computing power.
Advice for small AI teams renting GPUs
Start with your workload, not the chip. A short fine-tuning job, a steady inference endpoint and an overnight render batch each favour a different kind of provider. If you run short experiments, an on-demand cloud with fast start-up will save you the most frustration. If you serve a model all day, look harder at reserved capacity and how endpoints are handled.
Keep your setup portable. Use containers, keep model weights and datasets somewhere you control, and write down every step it takes to get a run going. That way, if a provider changes direction or a region runs short, moving is a day's work rather than a month's. For wider lists, see my Runpod alternatives and my guide to cloud GPU providers.
Questions to put to a GPU provider before you sign
- Will you rent a single on-demand GPU to a small team, or do you only sell reserved clusters?
- How long does a new instance usually take to start, and can I try it on my own workload first?
- Which regions can I run in, and where is my data stored between runs?
- Do my framework, drivers and libraries run as they are, or will I need to change my stack?
- What happens to my storage and running jobs if I stop or pause an instance?
- Who do I talk to when something breaks, and how do I reach them?
Do you make GPU cloud 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 should I check first when switching GPU clouds?
Check that the provider still rents single on-demand GPUs to small teams. Some providers now focus on large reserved deals, so confirm that before you compare anything else.
Are AMD GPUs a realistic option for AI work?
They can be, especially for teams willing to use ROCm. Test your own models and libraries on AMD before you move a production job, because support varies by framework and version.
What does sovereign compute mean?
It usually means the provider keeps your workloads and data on infrastructure in one country under that country's rules. Requirements vary, so check with your regulator if you handle personal or sensitive data.
Should I rent on demand or reserve capacity?
On demand suits short experiments and bursts of work. Reserved capacity suits steady, predictable workloads. Many teams start on demand and reserve once their usage settles.
How do I avoid getting stuck with one GPU provider?
Use containers, keep your weights and datasets in storage you control, and script your setup. Then a move to another provider is mostly a matter of running the same scripts elsewhere.
Can I use these GPU clouds for rendering as well as AI?
Many can, because rendering and AI both need GPU time. Check that the provider supports your rendering software and drivers before you plan a big batch.
