JupyterLab vs Deepnote: a side-by-side comparison for 2026

JupyterLab and Deepnote both give data professionals an interactive notebook environment for working with code, data, and visualizations.

Deepnote is a managed data workspace built around shared projects. A project can contain multiple notebooks that use the same integrations, files, dependencies, compute, and permissions, with real-time collaboration, Deepnote Agent, MCP, schedules, data apps, and local IDE development available around the same work.

JupyterLab is the open-source development interface in the Project Jupyter stack. It gives users direct control over kernels, extensions, files, environments, and infrastructure. Real-time collaboration and AI agents are also available through optional Jupyter packages.

JupyterLab vs Deepnote at a glance

What is Deepnote?

Deepnote is a collaborative data workspace built around notebook-based analysis.

A Deepnote project can contain several notebooks that share the same filesystem, data integrations, environment, compute, and permissions. An ML project, for example, can keep exploratory analysis, feature preparation, model training, inference, and an internal app in separate notebooks without setting each notebook up independently.

That project model also gives the notebook access to capabilities that would otherwise sit in separate tools:

• Deepnote Agent, schedules, data apps, APIs, and native data integrations work against the same project context.
• Teams can edit and execute notebooks locally through the Deepnote extensions for VS Code, Cursor, Windsurf, and Antigravity.

Deepnote Cloud handles the shared runtime, collaboration, permissions, and compute, while local development gives developers the option to work in their existing IDE.

For a comparison with the broader Jupyter ecosystem rather than JupyterLab specifically, see Jupyter vs Deepnote.

What is JupyterLab?

JupyterLab is the main web-based development interface in the Project Jupyter ecosystem. It expands the notebook experience into a workspace where users can open notebooks, terminals, text files, code consoles, debuggers, and other tools side by side.

Its main advantage is control. JupyterLab can run on a laptop, remote server, cloud VM, container, or a multi-user setup such as JupyterHub. Language support comes through kernels, and its extension system can change almost every part of the interface.

JupyterLab 4 also supports capabilities that were missing from older comparisons:

• Real-time collaboration can be enabled by installing jupyter_collaboration.
• Jupyter AI can connect agents such as Claude and Codex to notebooks, files, kernels, and MCP tools.

Those capabilities still depend on how the Jupyter environment is installed and configured. JupyterLab gives users the building blocks rather than prescribing a managed team environment around them.

Deepnote Agent vs Jupyter AI

Deepnote Agent works inside the notebook with the surrounding project as context. It can use the code, outputs, files, environment, and connected data already available in that project while working through a request.

Agent has two modes:

• Ask mode explains code, answers questions, and suggests approaches without changing the notebook (for a closer look at how this compares with Jupyter AI, including its chat, agents, notebook editing, and MCP support, see our Jupyter AI guide.)
• Edit mode can add, revise, or remove Python, SQL, and text blocks, execute them, inspect the output, and adjust its next step.

For larger requests, Agent shows the plan it is following and the changes it makes. Runs can also be undone afterward.

The same analytical projects are available to agents outside Deepnote through Deepnote MCP. Codex, Claude, Cursor, and other MCP-compatible clients can explore projects, create and edit notebooks, start runs, and inspect integrations under the permissions already configured in the workspace.

Jupyter AI 3.1 has also moved well beyond a notebook chat panel. It can connect agents such as Claude and Codex to JupyterLab, create and edit notebooks, execute cells through kernels, work with files, and use MCP tools. Users can configure the model providers, agents, and MCP servers they want in their Jupyter environment.

The difference shows up in how much context is already there. Deepnote Agent starts inside a project where the team's data connections, environment, files, notebooks, and compute are already configured. In JupyterLab, what an agent can access depends on the kernels, credentials, extensions, and services available to that particular deployment.

Deepnote vs JupyterLab for projects, collaboration, and infrastructure

A Deepnote project holds related analytical work in one environment. Several notebooks can use the same filesystem, installed packages, data integrations, secrets, permissions, and machine configuration.

Take a customer-retention project. One notebook might prepare warehouse data, another train and evaluate a churn model, and a third run inference or back an internal data app. Each notebook can work against the same underlying project resources instead of setting them up again.

That shared project model also covers collaboration:

• Teammates can edit the same notebook in real time and comment on individual blocks.
• Project permissions control who can view, comment, execute, edit, or manage the work.

JupyterLab gives users more control over how the shared environment is built. It can run on a laptop, a remote server, inside a container, or as part of multi-user infrastructure such as JupyterHub.

JupyterHub is often the next layer for teams that need separate notebook servers for multiple users. It handles spawning and authenticating user environments, while organizations can choose how those environments are isolated, what compute they receive, and which storage or packages are available to them.

JupyterLab 4 can add real-time collaboration through the jupyter_collaboration package.

Beyond that, the deployment determines how authentication, shared storage, credentials, package environments, and compute are handled. Teams can build those services around JupyterLab according to their existing infrastructure.

Data connections and SQL

Deepnote has native integrations for databases, warehouses, and storage services. Connections can be configured once in the workspace and attached to projects with their credentials managed separately from notebook code.

SQL is also a native notebook block in Deepnote. A notebook can move directly between a warehouse query, a Python dataframe, charts, and additional analysis without requiring the team to build its own SQL extension around the notebook.

A shared Snowflake or BigQuery connection can be reused across projects without every collaborator creating their own local configuration.

JupyterLab can connect to almost any data source Python, R, or another supported kernel can access. Teams typically manage the relevant SDKs, drivers, credentials, and environment configuration themselves.

That approach gives developers a lot of choice. A team can use SQLAlchemy, JupySQL, database-specific Python clients, or its own internal libraries, and the same notebook can connect to whatever services those tools support. The tradeoff is that connection setup, credential distribution, and driver compatibility live in the surrounding environment rather than being managed by JupyterLab itself.

Extensions and other Jupyter projects can improve this experience, but data connectivity is not a workspace-level service provided by JupyterLab itself.

Local development and notebook portability

The official Deepnote extension lets developers open, edit, and execute Deepnote notebooks in VS Code, Cursor, Windsurf, and Antigravity. Local execution uses the developer's Python environment, and SQL blocks can connect to supported databases from the editor.

Deepnote also supports importing and exporting Jupyter .ipynb notebooks. Teams moving from Jupyter do not have to recreate existing notebooks before bringing them into a Deepnote project.

Once an analysis needs to run repeatedly or reach somebody who does not work in the notebook itself:

• Scheduled runs and the Deepnote API can execute notebook workflows automatically or from another application.
• Data apps expose selected inputs, tables, charts, and outputs while keeping the notebook code behind the interface.

JupyterLab remains a natural environment for local notebook development. Users have direct access to their .ipynb files, kernels, terminal, packages, and operating system, and the notebook format works across a large number of tools.

JupyterLab also fits well into existing Python development setups because the notebook server runs against environments the developer already controls. Conda, venv, containers, remote kernels, and custom Jupyter kernels can all be used without changing the notebook interface, which is useful when a team already has a standard way to reproduce development environments.

Automation can be assembled from that surrounding ecosystem. Papermill or nbconvert can execute notebooks, cron or Airflow can schedule them, and tools such as Voilà, Streamlit, or Dash can turn analytical code into something other people can use.

As we discuss in the notebook manifesto, we think the useful notebook record includes the code, outputs, context, and execution history behind a result. Deepnote keeps those pieces with the project while giving teams several ways to run or publish the same underlying work.

For a wider look at other notebook environments, see our JupyterLab alternatives guide.

Deepnote vs JupyterLab pricing

Deepnote pricing includes three plans:

• Free: up to three editors and five projects, limited Deepnote AI, and Basic cloud machines.
• Team: $39 per editor per month when billed yearly, with unlimited viewers and notebooks, premium integrations, background execution, schedules, and monthly AI, CPU, and GPU credits.

Enterprise adds features such as permission groups, SSO and directory sync, audit logs, private Docker images, bring-your-own LLM, and single-tenancy.

The pricing models therefore cover different things. JupyterLab gives teams open-source software and leaves deployment decisions to them. Deepnote charges for a managed workspace that includes the collaboration, compute, data connectivity, and workflow features around the notebook.

JupyterLab itself is free and open source under the BSD license.

There is also no prescribed deployment model or paid tier attached to JupyterLab itself. An individual can run it entirely on a laptop, while organizations can host it on their own cloud infrastructure or use JupyterHub for multi-user access.

That does not necessarily mean a team Jupyter deployment has no cost. Compute, storage, cloud infrastructure, authentication, JupyterHub administration, environment management, and maintenance depend on how the organization chooses to run it.

Summary

Deepnote takes the notebook workflow and manages more of what surrounds it. Notebooks can stand on their own, while related notebooks can also be organized into projects that share integrations, compute, files, dependencies, and permissions. Agent, MCP, schedules, APIs, data apps, and local IDE development extend that work beyond exploratory analysis.

JupyterLab remains a capable open-source environment for interactive computing. It gives users control over kernels, infrastructure, extensions, files, and the broader Jupyter stack, and newer additions such as real-time collaboration and Jupyter AI have filled several gaps that existed in older comparisons.

For an individual who wants maximum control over a local or self-managed Jupyter environment, JupyterLab remains a good choice. For a data team that wants those surrounding capabilities already connected to the notebook, Deepnote provides the more direct path.