It suits people exploring data or developing machine-learning models in hosted Jupyter notebooks, including teams that need shared storage and collaboration controls.
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Best Notebook Software 2026
Notebook software combines executable code with a format for working through and sharing analysis. Compare execution models and supported languages with the notebook formats you need to create or open. GPU compute can be a relevant distinction for some workloads, while real-time collaboration and version history help frame how teams work together and revisit changes. Pluto.jl, Datalore, and Deepnote are among the options to consider. Free-plan availability and paid-from prices add cost context alongside runtime, format, and collaboration features, helping you assess which setup suits your notebook work.
Datalore
Datalore suits people and teams working with notebooks, databases and shared data reports.
WebLinuxAPI+1Pluto.jl
Pluto.jl is designed for students and teachers working with Julia notebooks.
AndroidWebWindows+4Deepnote
Deepnote suits data professionals and teams working with analysis, exploration, or machine learning who want collaborative notebooks, dashboards, or data apps.
WebWindowsMac+4JupyterLab
JupyterLab suits people working with notebooks and code in exploratory computing, including data science, scientific computing, computational journalism, and machine…
WebWindowsMac+3It suits data scientists, analysts, educators, engineers, and developers who want Python notebooks for data work or app building.
It suits researchers, data scientists, professors, scientists, and library authors who need reproducible notebooks or collaboration across languages.
Colab may suit students, educators, data scientists, AI researchers, and researchers who want to work with Python notebooks in a browser.
Community is listed for hobbyists and small projects, Professional for individuals, Team for companies collaborating on data, and Enterprise for organizations needing…
SageMath suits students, mathematicians and researchers needing broad mathematical tools across desktop, browser, command-line or self-hosted environments.
Zeppelin suits people building interactive analytics notebooks with supported languages and data backends.
JupyterLite suits people who want browser-based notebooks or a lightweight REPL without setting up a dedicated application server.
Dnote suits people who prefer terminal-based note capture and locally stored SQLite notebooks.
JupyterHub suits classes, research groups, data science teams, and organizations that need to provide individual computational environments to a group.
Observable suits people who want to explore data and explain ideas through interactive code notebooks, alone or with collaborators.
Polynote suits developers and data practitioners who need mixed-language notebooks, Scala, Spark, and local execution.
It may suit teams operating GPU infrastructure for inference, training, fine-tuning, or development workspaces.