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.
Oh, definitely clear that up! I always check the pricing page before signing up and that was an immediate nonstarter for me. Unlimited individual + 3 team projects seems more reasonable.
Okay. That is indeed more than just a free trial. I read the pricing and thought it was three projects, one of which is active, and seven days of revision history. (I also see you offer free team plans for academic use.)
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
The objections here would be trivially resolved if the pricing page said "Deepnote Standard is and always will be free" instead of "Deepnote is and always will be free".
Well, they aren't the first to say "always free" despite having features that can be paid for, and once you get used to this little piece of marketing speak, it's easy to understand.
I appreciate them using something commonly understood.
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!
What's your security model for logged-in users, one of the reason we (the jupyter team) have nbviewer on a separate domain with no login is to have embedded JS and other potentially sensitive content to be rendered without risk. We've seen people trying many attack vectors against renderer like this one with for example injecting script tags in things like prompt numbers.
Thanks for the q (I work at Deepnote) - all outputs that can contain potentially malicious JS are sandboxed in iframes so they can only access their local context and can't be used e.g. for XSS attacks.
That seems great, but for the comparison between DN viewer and the other, is there any benchmark tests specifically or it just a general feeling of speed when someone is using them. Because for each Jupyter notebook viewer it will depend on the setup, machine and the load itself.
Hi there! Simon (Engineer at Deepnote) here. We didn't benchmark the load speed but for some reason, Github's ipynb viewer has always felt to me quite slow and unreliable.
All viewers are publicly accessible so I'd love it someone did an independent benchmark. I'd prefer to avoid doing one ourselves because of the obvious conflict of interest
I thought that it is more than this. I think you should support any claim about that by creating a reproducible open benchmark test so at least people can understand what criteria you selected.
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.
