Deepnote
DeepnoteReviews from AWS customer
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Best notebooks I've found
What do you like best about the product?
I've used several different notebooks: jupyter, databricks, colab, kubeflow. Out of everything I have tried, Deepnote gives me what I need. It is easiest for collaboration, easiest for scaling appropriately, easiest for choosing different environments and getting set up. It is just what I need, and I hope they keep improving it in these practical ways.
What do you dislike about the product?
It costs a bit much for the first tier past free. I am an individual, and not comfortable paying so much per month just to collaborate with a few people. It should cost about half that, to be more comfortable to individuals. I'd pay $25 per month, not $50.
What problems is the product solving and how is that benefiting you?
I use it for whatever I need in data science (ML, AI, network science, prototyping)
Deepnote Makes Data Work Simple and Organized
What do you like best about the product?
I really enjoy using Deepnote with my team. I like how I can work across different notebooks and easily control access — for example, giving certain team members the ability to see results without touching or seeing the code. The ability to copy and share a link to a specific block is incredibly useful, and the overall organization makes collaboration seamless. Even though I’m not a coding expert, Deepnote feels very intuitive and user-friendly, which makes working with data much easier.
What do you dislike about the product?
So far, I haven’t found anything that significantly hinders my work — it meets my needs very well. However,sometimes performance slows with very large datasets, but it’s definitely not a frequent issue for me.
What problems is the product solving and how is that benefiting you?
Deepnote helps me keep all the code I need organized in one place and accessible to my team. It makes it very easy to share results, collaborate, and control who can view or edit the code. This saves time, keeps our work consistent, and improves team efficiency
Deepnote Makes Collaboration Effortless
What do you like best about the product?
- Multiple people can edit the same notebook at once, see each other’s cursors, and comment.
- No local configuration nightmares - packages and environments are managed in the cloud, so you can start working immediately.
- Built-in connectors to BigQuery, Snowflake, PostgreSQL, Google Sheets, etc., make it easy to pull data without juggling API keys and drivers.
- No local configuration nightmares - packages and environments are managed in the cloud, so you can start working immediately.
- Built-in connectors to BigQuery, Snowflake, PostgreSQL, Google Sheets, etc., make it easy to pull data without juggling API keys and drivers.
What do you dislike about the product?
- Processing very large dataframes can feel slower than a well-tuned local Jupyter setup.
- You can hit memory or runtime caps quickly if you’re doing heavy ML or large ETL work unless you upgrade to higher-tier plans.
- Converting notebooks to .ipynb or other formats works, but sometimes you lose small bits of formatting or Deepnote-specific features.
- You can hit memory or runtime caps quickly if you’re doing heavy ML or large ETL work unless you upgrade to higher-tier plans.
- Converting notebooks to .ipynb or other formats works, but sometimes you lose small bits of formatting or Deepnote-specific features.
What problems is the product solving and how is that benefiting you?
- Deepnote lets multiple people edit, comment, and run code together in real time, just like Google Docs, making cross-functional collaboration smooth.
- Deepnote’s cloud environment is ready to go, with easy package management and prebuilt integrations, so onboarding new collaborators takes minutes, not hours.
- Built-in connectors allow quick, secure access to live data without messy credential sharing.
- Deepnote keeps a clean version history and branching system built into the UI, so you can experiment and roll back without technical overhead.
- Deepnote’s cloud environment is ready to go, with easy package management and prebuilt integrations, so onboarding new collaborators takes minutes, not hours.
- Built-in connectors allow quick, secure access to live data without messy credential sharing.
- Deepnote keeps a clean version history and branching system built into the UI, so you can experiment and roll back without technical overhead.
Effective tool to visualise performance metrics
What do you like best about the product?
Very collaborative framework where multiple people can work on the same notebook
Interactive and intutive UI
Simple Data connector integrations for major databases
Interactive and intutive UI
Simple Data connector integrations for major databases
What do you dislike about the product?
Handling large data sets can result in slower processing of data
The free tier plan has few resources and collaboration limits that can be restrictive
The free tier plan has few resources and collaboration limits that can be restrictive
What problems is the product solving and how is that benefiting you?
KPI analysis of the Business metrics of our Latest Release patches is very easy and convenient.
Visualisation of the data becomes quite easy using deepnote for our KPI analysis.
Visualisation of the data becomes quite easy using deepnote for our KPI analysis.
Good experience with Deepnote
What do you like best about the product?
The option to create a query/ code and visualization in one place.
What do you dislike about the product?
That I can't see my tables details inside the deepnote.
What problems is the product solving and how is that benefiting you?
Queries and visualizations in one place
Great notebook and automation tool that i've been using as a member of the data and analytics team
What do you like best about the product?
Combining of SQL blocks and python code is very seamless and easy to use, i believe the user experience overall was pretty good it's easy for me to get the hang of it after just an hour of using and exploration. As well as how easy it is to integrate different platforms such as snowflake and Open AI to use into my projects
What do you dislike about the product?
the deepnote AI isn't too helpful at times i would give it explicit information for example not to change a specific part of the code yet it wouldn't read the prompt and change it anyway, it also has trouble sometimes reading the whole notebook but is okay with understanding a block. I would like an easier way for dependency blocks to be a thing where there could be a flowchart type flow from top to bottom where it doesn't have to trigger every single block created.
What problems is the product solving and how is that benefiting you?
Business automations and ai integration, the combination of SQL blocks and python code made it much easier for me to work on a solution
Love that I can rapidly pull data from different sources and build visualizations.
What do you like best about the product?
The workflow of integrating SQL and python is what I need for my data queries. I'm an embedded programmer and need to monitor the performance of various features across the devices in the field and this often involves pulling data from various data sources and then combining and correlating them. Doing this in a notebook that integrates with our data sources has enabled us to get ahead of issues and rapidly build dashboards for things that interest my team the most.
What do you dislike about the product?
Sometimes my SQL queries have common element (like CTE) that I use in a few different data projects. It would be nice to have some ability to have common code.
Another thing is being able to run a specific SQL query in a loop with slightly different parameters and build up a list of tables.
Another thing is being able to run a specific SQL query in a loop with slightly different parameters and build up a list of tables.
What problems is the product solving and how is that benefiting you?
Our devices push data to various tables in our data lake. In addition we have data pipelines that further create processed data. Being able to run multiple queries in and pull all that data to manipulate in Python is my primary usecase.
Best notebook with AI
What do you like best about the product?
I have really enjoyed the experience of using the AI from Deepnote. I used to use Hex before, and your AI is sensational. Another point is the lightweight interface; a problem I had was that when creating many cells (over 50), Hex would slow down, and with you, this hasn't happened.
What do you dislike about the product?
So far the only constructive criticism I have is that the UI could be prettier, maybe the color palette or the design of the boxes.
What problems is the product solving and how is that benefiting you?
I am a data engineer, so I model quite a lot in dbt. However, I often need to analyze the results of my models or explore a dataset, and deepnote helps me a lot with this because I can connect directly to Redshift and to my models to investigate further.
Collaborative and Insightful Data Analysis Tool with Room for Workflow Improvements
What do you like best about the product?
Deepnote is very intuitive and easy to get started with, which made the onboarding process smooth. It’s excellent for conducting analyses collaboratively and reaching conclusions together. Over time, the AI begins to understand my hypotheses and the structure of the database, which leads to increasingly relevant and insightful suggestions. It’s a powerful companion for exploratory analysis and decision-making.
What do you dislike about the product?
At the beginning, it requires more detailed explanations of your hypotheses and how the dataset works before the AI can provide useful suggestions. The prompts need to be very specific—for example, instead of saying “use the table above to create a scatter plot,” you need to reference the exact table name. Sometimes the AI freezes and runs indefinitely, requiring me to reset the notebook. I also find it difficult to stay organized when too many charts are generated, as I need to scroll a lot to find what I’m looking for. It would be very useful to have a dedicated space to organize the analysis into a document that combines written explanations with the corresponding charts.
What problems is the product solving and how is that benefiting you?
I am an advanced SQL user but have limited knowledge of Python. I know that many tasks—such as certain types of visualizations, projections, and data structuring—are easier to do in Python. Often, I already know the analysis I want to perform but don’t know how to technically implement it. Deepnote helps bridge this gap: by explaining my hypothesis and desired analysis in natural language, the AI can build the technical components for me.
It’s still important to review the outputs critically to ensure the results match my intentions. For this reason, Deepnote is especially valuable for users who already have an analytical background and strong critical thinking skills.
It’s still important to review the outputs critically to ensure the results match my intentions. For this reason, Deepnote is especially valuable for users who already have an analytical background and strong critical thinking skills.
Great Tool; UX can improve
What do you like best about the product?
Great tool for data visualization and creating dashboard
What do you dislike about the product?
UI is a bit finnicky; hard to lock onto a single point in a graph.
What problems is the product solving and how is that benefiting you?
Helping our Analysts create dashboards for specific features so we can track feature health.
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