Jupyter Notebook vs JupyterLab: Pick the right default

If you search “Jupyter Notebook vs JupyterLab,” you’ll land on a Stack Overflow thread from 2018 that frames them as two separate products you pick between. That framing made sense at the time. It doesn’t anymore.

Notebook 7, released in 2023, is built directly on JupyterLab components. The old “classic” Notebook UI (version 6 and earlier) now sits in maintenance mode, receiving only security patches through a compatibility layer called nbclassic. So when someone today says they’re “using Jupyter Notebook,” they’re likely running a JupyterLab-powered frontend without realizing it. The two projects have converged under the hood, even though they still present different interfaces.

That changes the question. Instead of “which product is better,” the useful question is: which interface should be your working default in 2026, and when does neither one fit?

Check out our “JupyterHub vs JupyterLab: They solve different problems” guide as well.

What you actually get with each interface

Jupyter Notebook gives you a single-document view. You open one notebook, you see your cells top to bottom, and the browser tab is the whole experience. There’s no file tree sitting on the left, no terminal panel you can split off, no way to view a CSV alongside your code. For a quick, self-contained analysis where everything lives in one .ipynb file, that simplicity is the point.

JupyterLab gives you a workspace. You get a file browser, tabbed editing across multiple notebooks, a terminal, a text editor, and drag-and-drop panel layouts; all in one browser window. If you’ve used VS Code, the mental model is similar: an IDE where the notebook is one of several document types you work with, not the only thing on screen.

Both interfaces read and write the same .ipynb files. You can open the same notebook in either one and nothing breaks. This is worth emphasizing because the decision feels higher-stakes than it is. You’re choosing a UI, not a file format.

Interface Comparison: Single-Document vs Workspace

When Notebook still makes sense

Notebook’s strength is that there’s nothing to learn beyond “run this cell.” If you’re writing a one-off data exploration, prototyping a function, or following along with a tutorial, the minimal interface keeps you focused. You don’t have to close side panels, configure a workspace layout, or figure out which tab has your output. You look at the notebook and that’s all there is.

This also makes it a reasonable default for teaching. When someone is learning pandas for the first time, the last thing they need is to accidentally drag a panel and lose their notebook view. Notebook’s lack of features is, in that context, a feature.

When JupyterLab becomes necessary

The moment your work involves more than one file, JupyterLab starts earning its complexity. A typical example: you have a notebook for analysis, a utils.py with helper functions, a data/ folder with CSVs, and you want a terminal open to run git commands. In Notebook, that means four browser tabs and constant switching. In JupyterLab, it’s one window with everything arranged side by side.

JupyterLab also handles project-level tasks that Notebook simply can’t. You can diff notebooks with the built-in JSON viewer, use extensions for git integration, and drag a cell output into its own panel for reference while you keep editing. Once your work crosses the line from “a notebook” to “a project,” JupyterLab is the obvious default.

Integrations and extensions: Where they differ

Both Notebook and JupyterLab support extensions; but the ecosystem maturity differs.

Notebook (Classic)

Notebook 7 technically supports JupyterLab extensions, but to the best of current documentation, most extensions are developed and tested primarily against full JupyterLab. If you want to add functionality, you're editing config files and hoping dependencies align.

• Limited extension model
• Nbextensions for UI tweaks
• Less active ecosystem
• Most innovation now happens elsewhere

JupyterLab

JupyterLab has a mature extension ecosystem that Notebook 7 doesn't match. The extension manager gives you one-click access to Git integration (jupyterlab-git), interactive debugging (debugger), language servers for autocomplete and hover docs, and execution time tracking.

• Full extension architecture
• Git integration extensions
• Debugger support
• Language servers
• The jupyter-collaboration extension for real-time editing
• AI assistants (e.g., Jupyter AI)

JupyterLab has effectively become the extension-first interface. If you care about integrations; git, LSP, AI tooling; Lab is where development energy is going.

Integration comparison:

The pattern: JupyterLab treats the notebook as one artifact in a development environment. Notebook treats the notebook as the only artifact, so IDE-adjacent features don't make sense in its UI.

The rendering tradeoff most people don’t know about

JupyterLab’s richer UI comes with a real cost that doesn’t show up in feature comparisons. To handle long notebooks efficiently, JupyterLab uses a rendering optimization called windowing. Instead of rendering every cell at once, it virtualizes the notebook; only the cells currently in your viewport get fully rendered, similar to how large lists work in modern web apps.

This is great for performance. A 200-cell notebook loads faster and scrolls more smoothly. But windowing creates side effects with certain output types. If a cell produces an iframe-based output (common with interactive plotting libraries) or a widget that needs to be in the DOM to initialize properly, the virtualized rendering can cause it to appear blank, render incorrectly, or lose its interactive state when you scroll away and back.

The official JupyterLab FAQ documents this and suggests setting the windowing mode to defer or none as a workaround. That fixes the rendering, but it gives back the performance gains you got from windowing in the first place. In practice, this means plot-heavy notebooks; the kind full of Plotly charts, Folium maps, or embedded HTML; can behave unpredictably in JupyterLab until you learn about and adjust this setting.

💡 Pro Tip: If you work with Plotly, Folium, or HTML-heavy outputs, set windowingMode to defer in a fresh Lab install before you start building long notebooks. It prevents subtle rendering confusion later. This fixes most rendering issues with interactive outputs, at the cost of slower load times on long notebooks.

It’s not a dealbreaker, but it’s the kind of thing that creates “works on my machine” confusion when one person has the setting and another doesn’t.

The recommendation

Default to JupyterLab. It matches where the Jupyter ecosystem is heading, it handles real projects better, and Notebook 7 already shares its internals anyway. If you’re doing quick, single-notebook work and prefer the minimal view, Notebook is still fine for that; but treat it as a specialized mode, not as the starting point.

💡 Pro Tip: If you prefer Notebook’s minimalism but want JupyterLab’s power, collapse the file browser (Ctrl+B) and use single-document mode. You can make Lab behave almost identically to Notebook while keeping its extension ecosystem.

Where Deepnote changes the conversation

So far, we’ve assumed your work is local and single-user. That assumption holds for many workflows; but it quietly breaks the moment collaboration or stakeholder delivery enters the picture. The Notebook-vs-JupyterLab decision assumes your work is local, single-user, and ends when you close the browser tab. For a lot of data work, that assumption breaks down fast.

JupyterLab is a strong single-user IDE. You can extend it into a collaborative environment; the jupyter-collaboration package adds real-time editing; but collaboration is layered on top of a tool designed to run on one person’s machine. There’s no built-in concept of permissions, review workflows, or shared execution state. If two people want to work on the same notebook, they need to coordinate environments, install the same extensions, and sort out kernel management themselves.

Deepnote starts from a different assumption: that data work is collaborative and presentation-oriented by default. Real-time editing, comments, role-based permissions, and a shared runtime aren’t extensions; they’re the baseline. What you see in your browser is exactly what your teammate sees, because you’re both working against the same managed environment. There’s no “it works on my machine” because there is no local machine in the equation.

The second distinction is around turning notebooks into something other people can use. In JupyterLab, making a notebook interactive for a non-technical audience typically means reaching for Voila, Panel, Dash, or Streamlit; each with its own framework, deployment story, and set of tradeoffs. The notebook is the starting point, but the app is a separate artifact you build and host.

In Deepnote, notebooks can be published as apps directly. You choose which blocks are visible (inputs, charts, results) and which stay hidden (data loading, transformations, debug cells). Input blocks; dropdowns, sliders, date pickers are native and reactive, meaning changing a value re-runs the dependent cells automatically. You don’t wire up callbacks or build a separate frontend. The notebook is the app.

Deepnote isn’t trying to win the Notebook vs Lab debate. It’s solving the problem that debate ignores. It makes it a different tool for a different problem. JupyterLab is where you go for local IDE power, deep customization, and full control over your compute. Deepnote is where you go when the end goal is a shared analysis, a stakeholder-facing dashboard, or a team workflow where environment setup shouldn’t be everyone’s problem.

The two aren’t mutually exclusive, either. Deepnote’s notebook format is open source (Apache 2.0) and supports round-trip conversion with .ipynb, so you can move work between Jupyter and Deepnote without getting locked in. There’s even a jupyterlab-deepnote extension for viewing .deepnote files inside JupyterLab (read-only), and a vscode-deepnote extension that lets users work with Deepnote projects from their preferred IDE.

Key takeaways

The Notebook-vs-JupyterLab question has a clear answer: JupyterLab is the modern default. Use Notebook when you want the simplest possible single-document flow.

But if your actual pain points are collaboration, environment reproducibility, or getting a notebook in front of someone who doesn’t write code, you’ve outgrown the local interface question entirely. That’s where Deepnote fits; not as a replacement for JupyterLab, but as the next step when the notebook needs to become something a team ships rather than something one person runs.

If you look at how real users rate these tools on G2’s Python IDE category, the differences in experience come through clearly. The Jupyter Notebook consistently rates slightly higher than Deepnote in overall user satisfaction (~4.6 vs ~4.5 stars), with users often praising its simplicity and ease of use for individual analysis. Meanwhile, Deepnote’s reviewers highlight collaboration and workflow features as key strengths; real-time editing, team workflows, and ease of sharing often surface in praise and product direction scores. Both appear in the same Python IDE category, but Deepnote also spans Analytics Platforms and Data Science & ML Platforms, signaling broader use cases beyond the classic notebook experience. This underscores the practical point: Notebook and Lab deliver strong individual exploratory workflows, but when team collaboration and shared deliverables matter, users tend to value Deepnote’s integrated experience more. (G2)

If you’ve read this far, you’re probably thinking beyond interface preferences. The next confusion people run into is “JupyterHub vs JupyterLab.” That’s a different category mistake entirely.

Read: “JupyterHub vs JupyterLab: They solve different problems*”*

References

• Build Jupyter Notebook v7 off of JupyterLab components (JEP 79)
• Jupyter Notebook project / maintained versions notes
• NbClassic timeline / security-fix support
• Stack Overflow canonical — Notebook vs Labz
• JupyterLab windowing mode issues/discussion
• JupyterLab FAQ (windowing / iframe side effects)
• Deepnote — Why we built a new notebook
• Deepnote format conversion docs
• Deepnote OSS repo
• jupyterlab-deepnote extension