- Cloud GPU providers compared
- Ten cloud GPU providers to consider
- Where to start for your project
- The NCSC's first cloud security principle
- On-demand instances, serverless and clusters
- Questions to ask before you rent a cloud GPU
- Signs it's time to rent GPUs
- My plain advice on choosing a cloud GPU provider
- Frequently asked questions
The best cloud GPU providers let you rent powerful graphics processors by the hour or longer, so you can train, fine-tune or run AI models without buying hardware. Some are huge AI clouds, others are simple platforms for individual developers. Ten options follow in no particular order.
A single high-end GPU can sit idle for most of the week in a small company. Renting one only when you need it can be the difference between trying an AI project and shelving it.
The ten providers below are listed alphabetically and in no particular order, and every description comes from the provider's own website, read this week. If cloud services in general are new to you, my explainer on what cloud hosting is and its benefits sets the scene.
Cloud GPU providers compared
I checked the free plan column on each tool's own website, as of Oct 2026. Yes means the site names a free plan, a free account, a free tier or an open source version. Trial only means a time-limited trial. Not stated means the pages I read named neither, even where there's a button inviting you to start for free, so check the vendor's page before you sign up.
| Tool | Type | Good for | Free plan |
|---|---|---|---|
| CoreWeave | AI cloud for training and serving models | Large-scale training clusters | Not stated |
| Hyperstack | On-demand cloud GPU service | On-demand GPUs by the hour | Not stated |
| Jarvislabs | GPU rental for AI and deep learning | Simple GPU rental with support | Not stated |
| Lambda | AI compute in the cloud | Teams training their own models | Not stated |
| Modal | Platform for production AI | Serverless inference and sandboxes | Not stated |
| Nebius | AI cloud from silicon to API | Self-service GPU clusters | Not stated |
| Paperspace | Cloud GPU platform from DigitalOcean | Existing customers of that cloud | Not stated |
| Runpod | AI developer cloud | GPU pods and serverless endpoints | Not stated |
| TensorDock | Cloud GPU platform | A wide choice of GPU types | Not stated |
| Vast.ai | Platform for renting GPUs from many hosts | Renting GPUs from many hosts | Not stated |
Ten cloud GPU providers to consider
CoreWeave
Best for: AI companies that need a cloud purpose-built for training and serving models at very large scale.
Hyperstack
Best for: Startups that want fast, on-demand cloud GPUs for AI and machine learning work, with hourly rates shown on the site.
Jarvislabs
Best for: Researchers and solo developers who want simple GPU rental for AI and deep learning with friendly support.
Lambda
Best for: Research and engineering teams that want cloud compute built around AI training, from single instances to clusters.
Modal
Best for: Developers who'd rather write code than manage servers, using serverless inference and sandboxes for AI workloads.
Nebius
Best for: Teams that want to go from zero to GPU clusters quickly with self-service access on a dedicated AI cloud.
Paperspace
Best for: Developers who already know DigitalOcean and want cloud GPUs for AI and machine learning workloads there.
Runpod
Best for: AI developers who want GPU pods and serverless endpoints across many regions, plus ready templates for open models.
TensorDock
Best for: Hobbyists and small teams who want a wide choice of GPU models for training, inference, rendering or gaming.
Vast.ai
Best for: Thrifty developers who want to rent GPUs from many independent hosts for AI, rendering or transcription work.
Where to start for your project
- Writing code without managing servers: Modal.
- Pods and serverless endpoints for open models: Runpod.
- Renting from many independent hosts: Vast.ai.
- Large training clusters: CoreWeave or Nebius.
- Developers already using DigitalOcean: Paperspace.
The NCSC's first cloud security principle
The National Cyber Security Centre's cloud security principles begin with data in transit protection. It says your data should be adequately protected against tampering and eavesdropping as it moves across networks inside and outside the cloud.
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That matters with GPUs because you're often sending your most valuable data and models to someone else's machines. Ask each provider how data is encrypted in transit and at rest, and what happens to your files when an instance is deleted.
On-demand instances, serverless and clusters
On-demand instances give you a whole GPU machine for as long as you keep it running. They suit training runs and experiments where you want full control.
Serverless GPUs start when a request arrives and stop when it's done, which suits AI features with uneven traffic. Clusters link many GPUs together for large training jobs that one machine can't handle.
Questions to ask before you rent a cloud GPU
- Which GPU models are available right now?
- Is billing per second, per hour or per month?
- Can I stop an instance without losing my files?
- Which regions are the data centres in?
- Is there serverless inference as well as full machines?
- What security and compliance details can they share?
Signs it's time to rent GPUs
- Training runs take days on a laptop. Move them to the cloud.
- You're about to buy a GPU for one project. Rent first.
- Your AI feature has spiky traffic. Look at serverless.
- Machines run overnight unused. Set them to stop.
Do you make a cloud GPU service? Tool makers can ask to be considered for a featured place on this list.
My plain advice on choosing a cloud GPU provider
Match the provider to the job. A weekend experiment needs a quick, simple rental. A product feature with real users needs reliability, support and security answers. A big training run needs clusters and a provider that can guarantee capacity.
And always switch machines off when you're done. Idle GPUs are the easiest way to waste money in the cloud. If you're still learning how the models themselves work, my plain guide to transformer models is a gentle introduction.
Frequently asked questions
What is a cloud GPU?
A cloud GPU is a graphics processor you rent from a provider over the internet, used for AI training, inference, rendering and other heavy computing.
Why rent a GPU instead of buying one?
Renting lets you use powerful hardware only when you need it, without a large upfront purchase or the work of running it yourself.
Are there free cloud GPUs?
Most providers charge for GPU time, though some offer credits or trials. Check each provider's own page for current offers.
What's serverless GPU?
Serverless GPU starts compute when a request arrives and stops afterwards, so you don't keep a machine running between jobs.
Is my data safe on a rented GPU?
It depends on the provider. Ask how data is protected in transit and at rest, and what happens when an instance is deleted.
Which GPU do I need for AI?
It depends on the model size and job. Larger models and training runs need more GPU memory, while smaller inference jobs can run on modest cards.
