JupyterLab vs. Jupyter Notebook: Which one should you actually use?

Jupyter Notebook and JupyterLab are both open-source tools for interactive data work.

There's a lot they do the same — they run the same .ipynb files, support Python, R, and Julia kernels, and give you a browser-based environment to mix code, visualizations, and markdown.

However, both tools struggle the moment your work involves other people. Collaboration means emailing files or pushing JSON to Git and hoping merge conflicts resolve cleanly.

In this article, we compare Jupyter Notebook and JupyterLab feature by feature, then look at what a modern alternative like Hex offers when your team needs real-time collaboration, governed sharing, and one-click publishing.

What is Jupyter Notebook?

Jupyter Notebook is a lightweight, single-document interface for solo exploration. It's relatively simple to learn, but doesn't come with any real-time collaboration features.

What it's like to use: Using Jupyter Notebook is relatively straightforward. You open a browser tab, see a column of cells, type your code, run the code, and see results. Every cell runs in order, and the entire narrative lives in a single .ipynb file. You can switch kernels to run Python, R, or Julia in the same window. The lightweight UI should barely tax your machine.

What works well: Notebook's strength is its simplicity. There's almost nothing to learn about the tool before you start typing. The minimal interface means fast loading and zero distractions.

What doesn't work well: Sharing and versioning get painful when your work starts to grow. File management is little more than an upload button, and the collaboration features are still stuck in 2011 — you push JSON to Git and hope merge conflicts behave. (The merge conflict, by the way, isn't even your fault. It's JSON comparing cell execution counts.) There's no way to work with teammates in real time. The problem isn't learning Notebook, it's outgrowing it.

What is JupyterLab?

JupyterLab is the IDE upgrade. You get shiny new tabs, terminals, visual debugging, and extensions. Real-time collaboration is available via the jupyter-collaboration extension, though it lacks granular permissions and governance controls.

What it's like to use: JupyterLab has extra functionality that lets you spread panels across your screen, drop a terminal in the corner, and open a CSV beside your code. You can drag panels around like building blocks. The side explorer lets you rename files, peek at CSVs, or open a second notebook for quick reference. Need shell access? Pop open the built-in terminal. Debugging a flaky loop? The visual debugger steps through variables just like a desktop IDE.

What works well: Extensions add Git integration, Tableau exports, or experimental real-time co-editing with a few clicks. The plugin manager usually handles any installation in minutes. This is perfect when you're juggling multiple data sources, writing utility scripts, or running deep experiments that demand terminals and side-by-side comparisons.

What doesn't work well: All those panels load more JavaScript, so older machines can start to slow down. Real-time collaboration is available via the jupyter-collaboration extension, but there's no granular permissions or audit trail — everyone with access can run code with equal power.

JupyterLab vs. Notebook: feature-by-feature comparison

JupyterLab wins on features while Notebook wins on simplicity. Here's where each one shines:

JupyterLab's real-time collaboration requires installing the jupyter-collaboration extension and lacks role-based permissions.

Use Hex when your team needs more than a notebook

Jupyter Notebook and JupyterLab work well for individual analysis. When your work involves other people or needs to reach stakeholders, you need capabilities these tools weren't designed for.

Hex provides the most advanced collaborative notebook environment available. It's an AI-powered platform where data teams and business users work side-by-side. Teams often find enough value in the notebook alone to adopt Hex, even before using the platform's broader capabilities.

Here's why Hex's notebook is the best on the market:

Real-time collaboration

Open a shared notebook and see your teammates' cursors moving live. No one waits their turn, no JSON merge conflicts. Tag a colleague with @ in a code cell, leave threaded comments beside a chart, or scroll back through version history when you need to know who changed the query. This is Google Docs-style collaboration built specifically for notebooks.

Publish your work

When your analysis is ready, press Publish. The notebook becomes an interactive app with filters and scheduled refreshes. Stakeholders get a link they can explore without seeing code. The same notebook handles exploration and stakeholder delivery without rebuilding.

Warehouse-scale performance

Computation happens in your warehouse, not your laptop. Connect Snowflake, BigQuery, or Redshift once. Heavy queries run server-side, so there are no browser crashes or no dependency drift.

Native AI assistance

Ask for "revenue by region, last 90 days," and the notebook agent builds the complete analysis. It reads your warehouse schema, suggests joins, generates visualizations, and writes production-ready code. All generated code is inspectable and editable, giving you a starting point you can refine rather than replace.

Because the AI is platform-native, it inherits the same permissions and governance model. No data leak risks from copy-pasting into external tools.

SQL + Python + No-code in one doc

Switch between SQL queries and Python transformations without leaving the notebook. Query results from SQL cells flow directly into Python for modeling. Pull data with SQL, transform it in Python, then add no-code components so stakeholders can explore on their own.

Jupyter can approximate this with plugins, but you're juggling environments and context-switching. Hex handles it natively.

Enterprise governance built in

SSO, granular permissions, and audit logs are standard. You decide who runs code and who only views results. Those rules apply to everything from queries to AI suggestions to published apps.

Who should use JupyterLab, Notebook, or Hex?

Use Notebook for learning, JupyterLab for solo power users, and Hex when your work involves other people.

Teaching a class? Quick plot on your laptop? Classic Notebook wins. Its single-document interface stays out of your way, loads fast, and leaves you with a single file to share.

The moment you need multiple files open, a terminal for quick pip install, or a visual debugger, you'll feel Notebook's constraints. That's where JupyterLab shines: its tabbed, drag-and-drop workspace lets you tile notebooks, Markdown docs, and consoles side-by-side.

But when your team spans time zones, needs audit trails, and wants to publish live dashboards for stakeholders, use Hex. Hex handles real-time editing, granular permissions, and one-click publishing in a single workspace.

Start using Hex today

Ask yourself: how many people touch your work? Who needs to use the results?

Both Notebook and JupyterLab leave you emailing files and fighting merge conflicts the moment you need to collaborate. Hex takes a different approach — real-time collaboration, governed access, and one-click publishing. When your data work needs to scale beyond individual exploration, that's where Hex fits.

Ready to see how Hex can make every person on your team a data person? Request a demo or sign up for your 14-day free trial to get started!