I've been using Deepnote for over a year now, mostly for Python projects. At first, I just used it to schedule and automate scripts for different tasks. As we grew, it turned into a go-to tool for running Python scripts, and even our non-technical teammates can now run scripts on their own without needing my help.

The interface is clean and easy to use, the built-in integrations are super handy, and having secret key management built in is a big plus.

I also really like the AI features — they help me write code faster by predicting what I need and are great for debugging when something goes wrong. The customer support also great, while they are not available during my working hours, but they will respond as soon as they are available. Review collected by and hosted on G2.com.

Sometimes the interface is a bit slow to load and can feel laggy. One thing I’m also concerned about is how secret keys are handled — it would be better if only the owner could view the actual values, while others could still use them without seeing the details.

I've also run into a few issues when moving scripts from VS Code to Deepnote. Even with the same Python version, some scripts that work perfectly in VS Code throw errors in Deepnote, which means I have to spend extra time debugging. Because of that, I’ve ended up relying on Deepnote more for testing just to avoid those additional time required. Review collected by and hosted on G2.com.

- 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. Review collected by and hosted on G2.com.

- 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. Review collected by and hosted on G2.com.

Site is easy to read and navigate. Editing reports is simple and straight forward. Review collected by and hosted on G2.com.

Running a query or report sometimes takes a little while although that may be more an issue with our data warehouse rather than an issue on your end. Review collected by and hosted on G2.com.

What I like most about Deepnote is that it feels like a supercharged version of Jupyter Notebooks. I use it daily at work to automate processes, connect to APIs, generate reports, and trigger actions. It’s very intuitive and user-friendly, even compared to other tools like Colab or Jupyter Lab. The built-in data visualizations are powerful and make analysis much easier. It was very easy to get started with—no complex setup or configuration was needed, so the ease of implementation was excellent. Everything just worked right out of the box.

On the few occasions I needed help, the customer support was responsive and helpful. Their team showed a great attitude and helped me resolve the issue quickly. Review collected by and hosted on G2.com.

Overall, I’m very satisfied and haven’t had major issues. I did run into some difficulty integrating Google Sheets directly, but it was likely due to a lack of documentation or research on my part. I worked around it using the Google API. Also, while I don’t use the collaboration features much, they could be useful for team-based projects. Review collected by and hosted on G2.com.

What I love most about Deepnote is its seamless real-time collaboration features combined with its intuitive interface. The ability to work simultaneously with team members on Jupyter notebooks while having built-in version control and commenting systems has completely transformed how we approach data science projects. The platform's integration with popular data sources and cloud services, along with its powerful compute resources, makes it incredibly easy to go from data exploration to production-ready insights without the usual infrastructure headaches. Review collected by and hosted on G2.com.

While Deepnote is fantastic overall, there are a few minor areas for improvement. The pricing can become steep for larger teams or when requiring extensive compute resources for extended periods. Occasionally, I've experienced slight lag during peak usage times, and some advanced customization options that power users might expect from local Jupyter environments are still limited. Review collected by and hosted on G2.com.

Deepnote makes pulling and exploring data so much easier. We can quickly connect to databases and get straight into analysis, which has really improved our visibility into performance and helped speed up decision-making. It’s also incredibly flexible — we’ve been able to build models that help ensure operational quality and adapt them as our needs evolve. Plus, the real-time collaboration, SQL/Python support, and zero setup make it perfect for remote teams. Review collected by and hosted on G2.com.

The main issue is the loading time when working with larger datasets or multiple data sources — it can be a bit slow to initialize. Also, since it’s fully cloud-based, it doesn’t work well in low-connectivity situations, and offline access would be a helpful addition. Review collected by and hosted on G2.com.

I've been using Deepnote for my Python projects, and it's been an absolute game-changer. The interface is clean, intuitive, and incredibly well-suited for both solo work and team collaboration. I especially love the real-time collaboration features—feels like Google Docs for data science. It also integrates smoothly with popular tools like GitHub and BigQuery, which saves me a lot of time. The fact that I can run Jupyter-style notebooks in the cloud without setting up a local environment is a huge plus. Highly recommend it for anyone working on data science, analytics, or machine learning workflows. Review collected by and hosted on G2.com.

sometimes a little slow but its a good feature to work with. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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 default:

- i should be able to query (join) every dataframe in SQL

- cell replay should replay its children by default Review collected by and hosted on G2.com.

Deepnote is a genuinely useful application of AI in coding, in terms of predictive code suggestions I've never come across anything like it and the time it saves building out queries is exceptional. It was an easy set up and integration and it's an intuitive interface that doesn't take long to familiarise yourself with. The file structure makes it easy to quickly run ad hoc queries on once off data sets while the multiple integrations and project structure make bigger projects a breeze. Would quite literally now be lost without it. Review collected by and hosted on G2.com.

The only things I dislike really are smaller nitpicks rather than feature gaps. The menu to convert/duplicate code blocks is a little clunky and the "fix my code" AI is not as effective as the predictive piece. Review collected by and hosted on G2.com.

The best thing is it does not have 10 minute limitations for running notebooks OR long-winded SQL quries. Review collected by and hosted on G2.com.

I would like to see an easy to access help guide for Deep note features and the link to be available in the Deep note hamburger menu. Review collected by and hosted on G2.com.