We wouldn't be the first to point out Google's monopolistic behavior in general but in the machine learning and deep learning world it is especially pronounced as Google services account for the vast majority of free GPU computing on the web.
Google operates Google Colab and Kaggle -- both of which are popular in the data science world and which offer free GPUs.
But times are changing! Google is raising prices. They continue to raise prices because it is the easiest way to keep buying their way into the market which is saturated with people using their stuff.
Google has scale but Google does not have selection when it comes to cloud GPUs. Google offers six different machine types (K80, P100, P4, T4, V100, and A100) which lags slightly behind the other cloud giants and lags majorly behind Paperspace which has more than twice as many options.
Google's Colab and Kaggle products both offer data scientists around the world a notebook environment to work with free GPUs. Paperspace does so too, via Gradient Notebooks. Once the free limit is exhausted, it's far easier to expand to better, more powerful GPUs with Paperspace.
It's incredibly difficult to get support as a Google Cloud customer. This is true whether or not GPUs are being used. While Google makes it difficult to talk to a human, Paperspace provides a team of support engineers 24/7 to provide direct, personalized support. Have a question about the performance of your cluster? Need help migrating data? Paperspace can help!
Not only does Paperspace have more instance types in more configurations, but Paperspace also provides a more developer-first and human friendly GPU computing experience. With Paperspace you can self-serve just about everything and there are people standing by to help you out when you get stuck.
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!"
