Choose a replacement by checking three things: which GPUs you can get when you need them, how fast a job starts, and how storage and networking are handled. Rental hubs suit quick experiments, developer clouds suit steady training and inference, and large-scale providers suit teams that need dedicated capacity.
From the homepage, almost every GPU cloud says the same thing: fast, flexible and built for AI. That makes them hard to tell apart when you're moving away from Verda, which describes itself as cloud infrastructure shaped by AI workloads. The differences show up in which GPUs are free when you need them and how your data gets to them.
Here are eight options for machine learning teams that rent GPUs, in no particular order, starting with a quick table.
Eight GPU clouds in a single table
| Tool | Kind of cloud | Good for |
|---|---|---|
| Crusoe | AI infrastructure cloud | Large AI workloads on energy-first infrastructure |
| Fluidstack | Large-scale AI compute | Very large compute builds and dedicated capacity |
| Hyperstack | On-demand GPU cloud | Starting AI and ML workloads on demand in minutes |
| Paperspace | Cloud GPU platform | Training, simulation and rendering on high-end NVIDIA GPUs |
| Runpod | AI developer cloud | On-demand GPUs plus serverless compute for inference |
| TensorDock | GPU server rental | Renting GPU servers for training, rendering or cloud gaming |
| Together | AI native cloud | Inference, fine-tuning and GPU clusters on one platform |
| Vast.ai | Pay-as-you-go GPU rental | Renting GPUs quickly for AI, ML and rendering jobs |
GPU providers, A to Z
Alphabetical and in no particular order. Every description reflects what the vendor says about its own service.
Crusoe
Best for: Teams deploying AI workloads at scale who want infrastructure from a company that describes its approach as energy-first, with round-the-clock support.
Fluidstack
Best for: Organisations planning very large compute capacity; the vendor's homepage focuses on building big data centres quickly in America.
Hyperstack
Best for: Developers who want on-demand cloud GPUs for AI and ML, with developer-friendly tools and the vendor's promise of a quick start.
Paperspace
Best for: Teams training models, running simulations or rendering on high-end NVIDIA GPUs, through a platform that now sits under DigitalOcean, as its own site shows.
Runpod
Best for: Developers who mix on-demand GPUs with serverless compute for training, inference and batch work; the vendor names a free plan.
TensorDock
Best for: Independent developers and small teams renting GPU servers for training, rendering or cloud gaming, on hardware the vendor calls enterprise-grade.
Together
Best for: AI product teams wanting inference, fine-tuning and GPU clusters on one full-stack platform, which the vendor ties to its own research.
Vast.ai
Best for: Researchers and builders who want to rent GPUs fast for AI, machine learning, deep learning or rendering, with the vendor stressing global availability.
Three questions that separate GPU clouds
Verda's own page talks about efficiency across software, compute, storage and networking. That's the right list, and it's the list to hold every alternative to.
First, which GPUs are available, and are they available now? A provider might list the card you want, but if it's always sold out in your region, it doesn't help. Second, how fast can you start a job? Time from sign-up to a running container, and from a stopped instance back to work, matters when you're iterating. Third, how are storage and networking handled? Large datasets need to sit close to the GPUs, and multi-GPU training needs fast links between them. Slow storage leaves the GPUs sitting idle, waiting on data.
Tools in this category split by who they're built for and how much you set up yourself. Rental hubs give you raw machines and leave the rest to you, while managed platforms handle more of the stack. Run the same real job through each before you decide.
Want AI doing the heavy lifting in your marketing?
I build the systems that handle the boring 80 percent, so you get your week back. Done properly, with the human kept in.
Rental hubs, developer clouds and cluster builders
Rental hubs
Vast.ai and TensorDock let you rent GPU machines quickly, which suits experiments, short training runs and rendering where you're happy to manage the environment yourself.
Developer clouds
Runpod, Paperspace and Hyperstack offer on-demand GPUs with more tooling around them, such as templates, serverless options or managed notebooks. They suit teams who train and serve models regularly.
Full-stack AI platforms
Together combines inference, fine-tuning and GPU clusters, which suits teams that want to serve models through an API as well as train them.
Large-scale capacity
Crusoe and Fluidstack focus on large AI infrastructure and dedicated capacity, aimed at organisations planning big, long-running workloads rather than one-off jobs.
Advice for ML teams moving off Verda
Don't move your whole workload on day one. Pick one training job and one inference job you know well, run them on two or three providers, and compare start-up time, throughput and how much setup each needed. Keep your container images and data loading scripts portable, so you can move again if availability changes.
If you mainly serve models, look at serverless and inference options before renting whole machines. For more providers, see my guide to the best cloud GPU providers, and for scale-to-zero serving, my round-up of the best serverless GPU platforms.
A first-job benchmark you can run in an afternoon
- Package one known training job in a container with a fixed dataset and seed.
- Launch it on each shortlisted provider using the same GPU type where possible.
- Record time to first running step, then steps per second once it settles.
- Copy a large dataset in and out to see how storage and network behave.
- Stop the instance, restart it, and note how long it takes to pick up again.
Do you make GPU cloud or AI infrastructure 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
What's the difference between a GPU rental hub and a GPU cloud?
A rental hub rents you machines, often from many hosts, and leaves setup to you. A managed GPU cloud adds tooling such as templates, storage and serverless options around the hardware.
Should I rent GPUs on demand or reserve them?
On demand suits experiments and irregular jobs. Reserved or dedicated capacity suits steady, long-running training where you need the same GPUs available every day.
Why does storage matter for GPU training?
If data loads slowly, the GPUs sit idle waiting for it. Check where datasets are stored and how fast they reach the machines before you commit to a provider.
Can I move my workloads between GPU providers?
Usually, if you keep jobs in containers and avoid provider-specific tools. Portable images and scripts make it far easier to switch when availability changes.
Do I need multi-GPU networking?
Only for training that spreads across several GPUs or machines. Single-GPU fine-tuning and inference usually don't need fast links between machines.
