Hey, the wording is not super clear there. Thanks for the feedback. Upon signing up, you get both a free individual account & can create free teams, just like in GitHub.
The free individual account has an unlimited # of created & running projects. The free team allows you to try out team-only features, but is limited in the number of projects.
Hey, Liz from the Deepnote team here. We don't actually have a trial plan. If you're an individual or a small team, you can use our standard plan for free forever. You only upgrade if you want to support a larger team, get stronger hardware or have Enterprise requirements. I'm sending more info about our plans & pricing here: https://deepnote.com/pricing
Thanks for the q! A follow-up comment here - if you look away from the publishing feature, the experience of Deepnote vs. Colab mainly differs in a) UI, b) breadth of integrations (Deepnote integrates with most of the data sources out there and plays well with the rest of your stack) and c) unlike Colab, Deepnote supports both real-time collaboration and asynchronous collaboration via comments - so quite literally Google docs / Figma meets notebooks.
Hey everyone, our team just built a tool to make your Github notebooks interactive and we would love for you to try it out. Just enter the GitHub URL of your (or any) public notebook and hit Render to instantly view the notebook. Your notebook will be rendered in an article-like layout and will get a table of contents. Anyone can fork it using the "Launch" button and play around with your code. The Deepnote viewer is also faster and more reliable than other .ipynb viewers we've tried. Would love for you to take it for a spin & hear your feedback!
No self-hosted/own cloud options yet. At the moment, managing the hardware ourselves allows us to be more agile and iterate on the product faster. But definitely something we're considering for the future, thanks for the comment!
Paperspace solves the pain around mlops, FloydHub improves deep learning workflows - both target purely technical users. The notebook of choice in both cases is Jupyter/JuperLab.
With Deepnote, our focus is improving usability of notebooks as a medium for both data scientists and non-technical users. We want to build a really good notebook experience that plays well with the rest of your stack and helps data science teams work better with the rest of the organization.
Hi from Deepnote! We've pulled GPUs temporarily so that we can focus the roadmap on improving the notebooks experience. Will be back sooner or later for sure.