Like Paperspace, CoreWeave provides an excellent variety of GPU machines -- from mid-tier GPUs up to the latest and greatest top of the line A100 80 GB GPUs.
Unlike Paperspace, however, CoreWeave makes it difficult to get started and access GPU machines. While it's understandable that computing providers need to promote strict measures against fraud, it is undoubtedly easier to get started with an account on Paperspace for free.
Paperspace makes it easy to see in the console how much various machines cost per hour and per month. Even users with a free account can see all the machines available, check out specs, and understand pricing. By comparison, CoreWeave only bills hourly. They are one of the few GPU cloud providers to do so.
Paperspace provides a number of helpful starter templates for deep learning applications such as the "ML in a Box" template. Paperspace makes it easy to get started with images that are pre-installed with useful ML libraries and dependencies.
Like CoreWeave, Paperspace has a wide assortment of GPU machines available as bare metal or with Linux or Windows pre-installed. But Paperspace also has Gradient, which is a suite of software specifically tailored to machine learning and deep learning users which contains Notebooks, Workflows, and Deployments. With Gradient it's easier than ever before to build and deploy deep learning applications on GPUs in the cloud with minimal fuss.
Paperspace and CoreWeave both operate data centers and provide state-of-the-art GPU compute in the cloud to users. Each company is providing a vertically integrated answer to the cloud giants like AWS, Azure, and GCP, with ultra-fast GPUs available to rent with minimal configuration and fuss.
But Paperspace, unlike CoreWeave, is focused on developers and teams. Paperspace allows you to create free accounts and provides options for free GPUs in Gradient. Paperspace also provides starter templates, cloneable projects, and a community of more than 500,000+ users who build GPU-powered applications on Paperspace.
Check out the Ultimate Guide to GPU Cloud Providers! It's all there!
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500K+
Users
100M+
Compute hours
1M+
Jupyter notebooks
"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!"
