Of the three major clouds (AWS, GCP, and Azure), Azure has the widest selection of GPUs. Although that doesn't mean these GPUs are available when you want them, Microsoft should be commended for offering a wide selection of options.
The tradeoff with any of the three major clouds is that it takes an extremely long time to get anything up and running. By contrast to the major clouds, Paperspace offers a wider selection of GPUs with a massively better user experience and customer experience.
Not only can you get started running GPUs in minutes on Paperspace, but you can also reach a team of friendly support engineers at all hours of the day.
If hyperscale GPU computing is the most important consideration, Azure may be a good bet. But if you prize simplicity over maximum configuration then Paperspace is worth a look.
One of the highest performing all-around deep learning GPUs at the moment is the NVIDIA A100 40 GB and 80 GB. Microsoft Azure prices these instances at almost ~$1 more per GPU than Paperspace! It's a little tricky to parse this out because Azure sometimes bundles instances into 8x-only clusters but the math is clear that Azure charges a premium for the high-end compute instances.
It can be difficult to get up and running on Microsoft Azure. Paperspace provides a number of templates and starters directly in the console to help you get started doing machine learning or deep learning in the cloud.
In addition to GPU-backed virtual machines, Paperspace also offers a software stack called Gradient which runs on top of these machines. Gradient provides deep learning users with Notebooks, Workflows, and Deployments to make it easier than ever to explore, train, and deploy deep learning applications.
Although Azure has the widest selection of any of the major clouds when it comes to GPU selection, Azure is not (by any stretch of the imagination) a "user friendly" service. Getting started is tough. Getting support is tough. Getting help is close to impossible.
By contrast, Paperspace has a stronger selection of GPUs which are also available at scale and offers world-class support and helpful resources.
Check out the Ultimate Guide to GPU Cloud Providers! It's all there!
Or do you have a question about this comparison that isn't answered? Please let us know!
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"For ML applications, I’ve found @HelloPaperspace to have the best UI / UX by far"
"I'm very impressed with @HelloPaperspace GPU cloud and an ability to create templates. One API call and 5 minutes later I'm training physics-informed neural networks through http://SIML.ai's environment (@nvidia Modulus + JupyterLab + VS Code + Tensorboard + netdata)."
"Have been using @HelloPaperspace Gradient Notebooks and it has been an amazing experience so far. ... A true local-like development environment feel 😄"
"I just checked out @HelloPaperspace and wow its soooo beautiful"
"I came across a very exciting feature on Paperspace: they mounted additional storage to every machine for free. That storage has public machine learning datasets. OMG, this is so cool. Great job @HelloPaperspace!!! 👏"
"Trying out @HelloPaperspace after all the problems with colab so far the transparency about what you're getting for your money (and what instances are available) is nice. But all the system information graphs are my favorite."
"Just tried Gradient from @HelloPaperspace. Man that thing is super easy to use. #MachineLearning #CloudComputing"
"First time using @HelloPaperspace. Great way to spend more time learning and practicing ML rather than debugging / setting up a Cloud instance."
"We're testing deployment to @HelloPaperspace GPU cloud. So far it works great! Next week we'll add possibility to launch http://SIML.ai instance on it through Model Engineer - one click and you'll be up-and-running!"
