Deepnote
DeepnoteReviews from AWS customer
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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
Incredibly useful tool
What do you like best about the product?
A quick and easy way to filter and review data. I’ve been using Deepnote daily for the past couple of weeks, adjusting parameters as needed to extract the most relevant datasets for my work.
What do you dislike about the product?
Nothing I can think about at the moment.
What problems is the product solving and how is that benefiting you?
I’ve used Deepnote a few times now, primarily for filtering and reviewing datasets, and it’s been a huge time-saver. Even without going deep into complex coding, I’ve found it really intuitive to use, especially when trying to quickly understand which data is usable for my goals and which isn’t. It’s made data review feel a lot less overwhelming and much more efficient. I haven’t explored all its features yet, but for what I need, it’s been incredibly helpful.
My experience as an Account Manager with Deepnote
What do you like best about the product?
Deepnote has been one of the most useful tools in my daily workflow.
As an Account Manager in a SaaS company, having access to accurate information is essential for making decisions. Deepnote has helped me make data-driven decisions by providing clear numbers and actionable insights, allowing me to rely on facts and accurate projections.
As an Account Manager in a SaaS company, having access to accurate information is essential for making decisions. Deepnote has helped me make data-driven decisions by providing clear numbers and actionable insights, allowing me to rely on facts and accurate projections.
What do you dislike about the product?
Sometimes, it takes longer than it should to load the data, especially early in the mornings.
What problems is the product solving and how is that benefiting you?
When I joined the company, I was basically blind when it came to my accounts.
I had no real context, just some feedback from coworkers, so the lack of information was my biggest pain point.
Deepnote allowed me to identify behavioral patterns across my accounts and begin using data to proactively reach out to clients. As a result, I was able to increase revenue starting from my very first month.
I had no real context, just some feedback from coworkers, so the lack of information was my biggest pain point.
Deepnote allowed me to identify behavioral patterns across my accounts and begin using data to proactively reach out to clients. As a result, I was able to increase revenue starting from my very first month.
allows us to house data cleanly!
What do you like best about the product?
love how it pulls tables for us. Allows us to share data internally.
What do you dislike about the product?
it might be a little bit slow but works well.
What problems is the product solving and how is that benefiting you?
sharing data internally
It is great for viewing landing page test results
What do you like best about the product?
My team uses Deepnote to track landing page test results, and the platform makes it super easy to pick out insights from the data provided
What do you dislike about the product?
I don't have any downsides! I think if we could see more charts and data visualizations, that would be great.
What problems is the product solving and how is that benefiting you?
We often want to test how different ad landing pages affect ad conversion rates, and Deepnote makes it very easy to set up these tests and view results.
easy to use
What do you like best about the product?
Deepnote is great because it lets multiple people work on the same notebook at the same time, just like Google Docs. It connects easily to tools like BigQuery, Google Sheets, and other databases without complicated setup. Since it runs in the cloud, there's no need to install anything—your work is always saved and accessible anywhere.
What do you dislike about the product?
Sometimes, Deepnote can be slower to load or run heavy computations compared to local notebooks. It also requires a stable internet connection since it's fully cloud-based. Lastly, while it's great for collaboration, some advanced users may find it less flexible than using raw Jupyter in their own environment.
What problems is the product solving and how is that benefiting you?
Deepnote solves the problem of collaboration in data science by allowing multiple users to work on the same notebook in real time. It also makes it easier to connect to data sources and share insights without switching tools. This helps teams work faster, stay organized, and make data-driven decisions more efficiently.
The interface has been very interactive and the processing speed has been impressive.
What do you like best about the product?
Ease of sharing codes with colleagues and faculty
What do you dislike about the product?
The default projects that load with every new workspace
What problems is the product solving and how is that benefiting you?
Energy data analysis
A good alternative to Google Colab
What do you like best about the product?
The pricing is very good and all platform works very well, there are different machines you can choose from.
What do you dislike about the product?
I have been facing problems using the google drive integration. It supposed to be very simple but i keep getting errors. There is not much tutorials online about deepnote.
What problems is the product solving and how is that benefiting you?
Training models of computer vision
Deepnote: is it worth the switch?
What do you like best about the product?
Supports python,SQL, integrates with GitHub, snowflake, BigQuery..
It is ideal for classrooms, portfolio projects and teams.
No setup needed.
It is ideal for classrooms, portfolio projects and teams.
No setup needed.
What do you dislike about the product?
There’s limited offline use.
Some features are behind a paywall
Some features are behind a paywall
What problems is the product solving and how is that benefiting you?
It helps solve problems like disconnected tooling, complex setup and environment management, lack of real-life collaboration in Data science Notebooks.
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