Find Open-Source Alternatives
Discover powerful open-source replacements for popular commercial software. Save on costs, gain transparency, and join a community of developers.
Discover powerful open-source replacements for popular commercial software. Save on costs, gain transparency, and join a community of developers.
Compare community-driven replacements for CoCalc in notebook & data science platforms workflows. We curate active, self-hostable options with transparent licensing so you can evaluate the right fit quickly.
Recent commits in the last 6 months
MIT, Apache, and similar licenses
Counts reflect projects currently indexed as alternatives to CoCalc.
These projects match the most common migration paths for teams replacing CoCalc.
Why teams pick it
Launch quickly with streamlined setup and onboarding.
Why teams pick it
Built‑in UI widgets and AI‑assisted cell generation
Reactive Python notebooks that stay reproducible, git‑friendly, and deployable
Why teams choose it
Watch for
Learning curve for the reactive model
Migration highlight
Exploratory data analysis with live filters
Analysts adjust sliders and instantly see updated visualizations, keeping results consistent and reproducible.
AI‑focused extensions that turn JupyterLab into a pipeline hub
Polyglot notebook with IDE‑grade editing for Scala, Python, SQL
AI‑enhanced Jupyter alternative with seamless code assistance
Why teams choose it
Edit Jupyter notebooks as plain text scripts for seamless version control
Scalable multi-user hub for Jupyter notebooks
Next-generation interactive computing environment for notebooks, code, and data
Teams replacing CoCalc in notebook & data science platforms workflows typically weigh self-hosting needs, integration coverage, and licensing obligations.
Tip: shortlist one hosted and one self-hosted option so stakeholders can compare trade-offs before migrating away from CoCalc.
Why teams choose it
Watch for
Debugger is marked experimental and may be unstable
Migration highlight
Create and run end‑to‑end ML pipelines
Design visual pipelines, connect notebook steps, and execute them on remote clusters with a single click.
Why teams choose it
Watch for
Experimental project may lack long‑term stability
Migration highlight
Exploratory data analysis with Scala and Spark
Combine Scala's type safety with Spark's distributed processing in an interactive notebook.
Watch for
AI features depend on external model APIs, which may incur costs
Migration highlight
Rapid prototype generation
Generate boilerplate functions or visualizations in seconds using the AI prompt, reducing initial coding time.
Why teams choose it
Watch for
Notebook outputs are not stored in text formats
Migration highlight
Version‑controlled notebooks in Git
Store .py versions in Git, get clean diffs, and regenerate .ipynb outputs locally.
Why teams choose it
Watch for
Default PAM authentication requires privileged setup
Migration highlight
Classroom notebook server
Students log in with university credentials and receive isolated notebook instances for assignments.
Why teams choose it
Watch for
Requires Python environment setup
Migration highlight
Exploratory data analysis
Run notebooks, visualize results, and edit scripts side‑by‑side for faster insight generation.