Deepnote
DeepnoteReviews from AWS customer
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It has nice AI features for a web-based notebook
What do you like best about the product?
It provides great visualization tools for notebooks, with some nice features like column summarization. It also lets you group many notebooks in the same project, something that its competitors don't let you do. Its AI features are better than competitors too, it can create a full analysis in its own.
What do you dislike about the product?
Despite the fact that the AI is way better than its competitors, it's still somewhat clunky to use, can create many unused cells and leave them there. Also, some features are missing/are not the defaul:
- i should be able to query (join) every dataframe in SQL
- cell replay should replay its children by default
- i should be able to query (join) every dataframe in SQL
- cell replay should replay its children by default
What problems is the product solving and how is that benefiting you?
Create business analysis and sharing it with stakeholders
A great way to collaborate for data analysts and scientists
What do you like best about the product?
We've been using Deepnote for a good while now, and my favourite feature is the ability to quickly and easily share code and visualisations with colleagues. This is really important to us as we use Deepnote for all our deeper A/B testing analyses, so being able to share results within the analytics team and also with product teams is super important. Recent AI-enabled features are a great add-on, although I'm yet to explore these fully. I also love being able to load in data from multiple sources and combine them in my notebooks.
What do you dislike about the product?
One thing that could be improved in Deepnote is the ability to find and store notebooks in projects more easily. With many analysts and scientists all using Deepnote, our folder structure has become pretty messy, especially since we don't have strong naming conventions for files. We have a lot of "Untitled Projects" laying around - some automation to help organise and tidy this up would be a great addition to a strong tool.
What problems is the product solving and how is that benefiting you?
We use Deepnote for all of our in-depth analytics work, especially around A/B testing (power calculations, simulations, impact analysis, correlations etc.)
Love the overall vision, implementation and integration
What do you like best about the product?
Multiple Notebooks is probably my favourite feature, helps keep the project modularised and accessible. Building data apps is cool too. And the wide data source integration.
What do you dislike about the product?
Limited customisability for tables and graphs. Also, hard to securely embed into Notion. The ability to add custom branding would be great.
What problems is the product solving and how is that benefiting you?
Collaboration and ease of production for data dashboards and apps. Earlier, it used to live on regular IDEs which had no customisability and creating a data app meant going through NextJS, or steamlit, both of which had immense learning curves and hard deployment.
Deepnote: Seamless Collaboration and Effortless Cloud-Based Data Science
What do you like best about the product?
Deepnote has a simple, unobtrusive UI that keeps the buttons out of the way, allowing you to focus on coding. The real-time collaboration features are outstanding, making it easy for multiple people to code together simultaneously. The main reason I prefer Deepnote is that it provides a cloud-based space for all my data science work, making it convenient to store, share, and manage even large files.
The Deepnote team pays great attention to user convenience. For instance, after using pip to install a package, Deepnote prompts you to add it to requirements.txt automatically. Then, whenever you restart the machine, it will auto-install that package for you. These small touches save a lot of time and mental effort.
The Deepnote team pays great attention to user convenience. For instance, after using pip to install a package, Deepnote prompts you to add it to requirements.txt automatically. Then, whenever you restart the machine, it will auto-install that package for you. These small touches save a lot of time and mental effort.
What do you dislike about the product?
I find that Deepnote’s two main drawbacks are its slow performance and the constant need for an internet connection. I often experience lag, especially when collaborating with others or working on large, complex projects. This sluggishness disrupts my workflow and takes away from the collaborative strengths that drew me to Deepnote in the first place. Also, since Deepnote operates entirely in the cloud, I have to stay connected to the internet at all times. If my internet connection drops, I immediately lose access to my projects and data—unlike when I use local tools like Jupyter Notebooks, which let me work offline. These limitations sometimes interrupt my productivity and make Deepnote less suitable for me when I need instant feedback or the flexibility to work without an internet connection.
What problems is the product solving and how is that benefiting you?
The two biggest benefits Deepnote gives me are seamless real-time collaboration and effortless access to my projects in the cloud. I love being able to work together with teammates in the same notebook, instantly sharing ideas, code, and results—this truly boosts our productivity and makes teamwork easy. At the same time, having all my data science projects accessible from anywhere means I never have to worry about setting up my environment or managing files locally; I can just log in and get to work, no matter what device I’m using. This combination makes my workflow simpler, faster, and much more flexible.
Intuitive UI, easy for spinning up long running tasks
What do you like best about the product?
Easily allowed our data and engineering teams to collaborate on generating datasets, simple UI that makes it easy to spin up long tasks
What do you dislike about the product?
Nothing major -- there's some small product changes:
* Pinning block actions at the top. The notebook actions (run etc) are sticky as you scroll, but oftentimes I wanted to just re-run a single block, but I'd end up re-runnning the whole notebook
* If a block writes to a file, link to that file in the output section
* Pinning block actions at the top. The notebook actions (run etc) are sticky as you scroll, but oftentimes I wanted to just re-run a single block, but I'd end up re-runnning the whole notebook
* If a block writes to a file, link to that file in the output section
What problems is the product solving and how is that benefiting you?
Platform for data and engineering teams to collaborate
Very efficient tool
What do you like best about the product?
I really enjoyed using the platform, especially the integrated AI. I found the suggestions it provides to continue analyses very relevant and helpful, particularly when dealing with ambiguous scenarios. Another aspect I appreciate is that when the AI doesn't reach a satisfactory answer, it makes another attempt instead of stopping. Additionally, I value the auto-completion feature for coding, as it significantly speeds up the workflow.
What do you dislike about the product?
One point I believe could be improved is the need to frequently re-run the code because the tables are no longer saved. It feels like the time before they expire is too short. Another improvement would be the addition of a pivot table option in the available charting features. Lastly, having a functionality to hide code cells that we no longer need to edit would make the workspace much cleaner and easier to navigate.
What problems is the product solving and how is that benefiting you?
Before using Deepnote, I worked with another platform that also offered integrated AI and similar visualization tools. However, the major differentiator for me is Deepnote’s AI capabilities. While the previous platform had AI, its performance was inconsistent and often inaccurate.
Deepnote, on the other hand, provides a highly coherent and reliable AI experience, with very few instances of hallucinations or irrelevant suggestions. This makes my workflow more efficient, as I can trust the AI's outputs and recommendations, reducing the time spent double-checking or reworking results. Overall, this significantly enhances the quality and speed of my analyses.
Deepnote, on the other hand, provides a highly coherent and reliable AI experience, with very few instances of hallucinations or irrelevant suggestions. This makes my workflow more efficient, as I can trust the AI's outputs and recommendations, reducing the time spent double-checking or reworking results. Overall, this significantly enhances the quality and speed of my analyses.
Very practical for team development
What do you like best about the product?
I love how I can create a shared environment with my teammate, and we don't have to worry about staying in sync. We have real-time visibility into what the other is doing and can set up duplicate notebooks and roll back changes easily. Jupyter is great on its own for code generation; it's that much more valuable in a shared environment.
What do you dislike about the product?
The startup is very slow, as it loads all our dependencies. This wouldn't be such a big deal if the machine didn't shut down every 15 minutes. In theory, it's supposed to load the cache, but I haven't gotten that to work. I also could not get the integration with Google Drive to work. It was showing an error, so I ended up using an API instead.
What problems is the product solving and how is that benefiting you?
It allows rapid prototyping and code development in a notebook environment.
One of the best AI assistants, but with a lot of improvements to be done
What do you like best about the product?
Deepnote has a very easy implemantation; It's ease to use and very useful, with a good capacity to generate insights.
What do you dislike about the product?
Deepnote does not permit to create analysis based on previous datasets; It often hallucinates; It does not obey the instructions, ex: Mmake this analysis in SQL instead of python or generate just this KPI (often creates excessive useless analysis)
What problems is the product solving and how is that benefiting you?
How to increase the productivity of our data analysts and business analysts.
Powerful, reusable, and simple
What do you like best about the product?
I really appreciate the level of control Deepnote gives me when running analyses and creating internal user-facing data apps. It's easy to reuse code blocks, connect to external data sources, and spin up visualizations in a matter of minutes. I've been using some version of Python notebook tools for ~15 years and I find Deepnote to be quite intuitive for someone like myself.
What do you dislike about the product?
The number one challenge I've had, and this is probably based on me being newer to Deepnote (having used Hex pretty extensively over the past 4 years), is project management. For example, created a notebook in the wrong project and I could not find a simple way to export that notebook from one project into another -- I ended up having to copy the whole project and delete everything I didn't need. In the same vein, not being able to have more than 1 live data app per project seems strange. For example, I build a notebook app and wanted to create a version for internal metrics that provides an overview of all clients, but then have a version that is per-client specific. Those need to live in different projects to be published. Perhaps it's just me not being as familiar with the project management aspect of Deepnote, but it felt like a bit of overhead that can be simplified with some UX work to improve the "speed to value" for users that create multiple data apps.
What problems is the product solving and how is that benefiting you?
Gives us a separate environment to do evals on our AI tools (fine tuning comparisons, hallucination reviews of LLMs, etc). It's collaborative aspect gives a simple way to host results and share amongst our small team.
It has been good - expect more AI capabilities
What do you like best about the product?
The app feature is pretty good - the interchange between languages SQL, python etc has also been good.
What do you dislike about the product?
I feel like with agents like claude code and cursor - you need a deeper AI stack in the platform to make it stand out.
What problems is the product solving and how is that benefiting you?
Analyzing data at scale
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