Is marimo free to use?

Yes, marimo is open-source and free to use under the Apache 2.0 license. There is no cost for self-hosting or using it locally. A managed cloud version may be available in the future, but the core product remains free.

Does marimo have a free plan or trial?

Marimo is entirely free and open-source, so there is no trial period or paid plan required. You can download and use it immediately without any cost.

How much does marimo cost?

Marimo is free to use as an open-source tool. There are no subscription fees or licensing costs. You only need to pay for hosting if you deploy it on a cloud server.

What are the best alternatives to marimo?

Alternatives to marimo include Jupyter Notebook, JupyterLab, Observable, Deepnote, and Hex. Marimo differentiates itself with reactive execution, git-friendly .py storage, and built-in SQL support.

Who is marimo best for?

Marimo is best for data scientists, analytics engineers, data engineers, researchers, and educators who need reproducible, interactive notebooks with reactive execution and SQL integration. It's also great for teams deploying data dashboards as web apps.

Who should avoid using marimo?

Marimo may not be suitable for users who prefer traditional IDEs like VS Code without notebooks, or teams heavily invested in Jupyter extensions that lack marimo compatibility. It's also not ideal for pure no-code platforms like Retool or for projects requiring real-time collaboration like Google Colab.

What are the key limitations of marimo?

Marimo lacks real-time collaborative editing (like Google Colab) and does not support all Jupyter extensions. It may have a learning curve for users accustomed to Jupyter's execution model, and deployment as a web app requires additional configuration.

What integrations does marimo support?

Marimo integrates with VS Code and Cursor via extensions, supports AI agents through MCP (Model Context Protocol), and includes built-in SQL cells for DuckDB, PostgreSQL, MySQL, SQLite, and other databases.

Is marimo easy to learn and set up?

Marimo has a moderate learning curve, especially for users transitioning from Jupyter. Installation is straightforward via pip. The reactive execution model and SQL cells may require some adjustment, but the documentation and community support are helpful.

Is marimo good for data science?

Yes, marimo is excellent for data science due to its reactive execution, interactive widgets, and reproducibility. It supports SQL and Python seamlessly, making it ideal for data analysis, visualization, and experimentation.

Is marimo good for building dashboards?

Yes, marimo allows you to deploy notebooks as web apps using built-in UI widgets and deployment options like WASM HTML and CLI. It's a good choice for quickly turning analyses into interactive dashboards.

Can marimo be used for SQL queries?

Yes, marimo has built-in SQL cells that support DuckDB, PostgreSQL, MySQL, SQLite, and other databases. You can seamlessly mix SQL and Python in the same notebook.

marimo

Freemium

Reactive Python notebook: reproducible, git-friendly, deployable as apps.

By Tanmay Verma, Founder · Last verified 03 Jul 2026

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Added 7d ago

77/100Safe Bet

Visit Website

In short

marimo — Reactive Python notebook: reproducible, git-friendly, deployable as apps. Best for Data scientists needing reproducible, interactive notebooks, Analytics engineers bridging SQL and Python, Data engineers building interactive tools and pipelines. Free to use.

Compared withvs Versatile vs Geologicai vs Screenplayiq

Is marimo actually worth it?

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Editorial Verdict

Best for

Data scientists needing reproducible, interactive notebooksAnalytics engineers bridging SQL and PythonData engineers building interactive tools and pipelinesResearchers and educators creating shareable computational narrativesTeams deploying data dashboards without rewriting

Not ideal for

Users who prefer a traditional IDE or script-only environmentProjects requiring closed-source or managed commercial notebook serviceAnalysts needing a pure no-code/low-code platform like RetoolTeams heavily invested in Jupyter extensions and workflowsUsers who need real-time collaboration (e.g., Google Colab style)

marimo is a strong open-source alternative to Jupyter that solves hidden state and git productivity. Its reactive model and pure Python storage are game-changers for reproducibility. While not for traditional IDE users, it's a top pick for data scientists and engineers needing interactive, deployable notebooks.

Compare with: marimo vs Quadratic, marimo vs Bito, marimo vs Formula Bot

Last verified: July 2026

What's new in marimo

Checked 7 days ago

Across the latest 10 updates: 4 feature updates, 2 launches, 2 changelog entries and 2 news mentions.

LaunchBlog·7 days agoNewest

We're running a notebook competition with alphaXiv — now on GPUs

Marimo notebook competition with alphaXiv, GPU support, prizes. Enter by July 8, 2026.

FeatureBlog·Jun 1

molab now has GPUs!

Public preview of molab on CoreWeave Cloud with GPU support.

NewsBlog·Jun 1

How Vodacom uses marimo to drive operational intelligence across 15,000 mobile network sites

Vodacom adopts marimo for operational intelligence across 15,000 mobile network sites.

NewsBlog·May 1

Announcing our notebook competition winners!

Marimo announces winners of its notebook competition.

FeatureBlog·Apr 1

Introducing marimo pair

Canvas for agents and developers collaboration; structured working memory and reactive Python runtime for agents.

LaunchBlog·Apr 1

marimo Learn relaunches with over eighty interactive notebooks

Marimo Learn relaunches with 80+ interactive notebooks.

ChangelogBlog·Feb 1

marimo 0.20.0

Reactive Matplotlib selections, matrix inputs, rich PyTorch display, and more.

FeatureBlog·Feb 1

Run marimo notebooks on any cloud with SkyPilot

Marimo notebooks run on any cloud via SkyPilot; no rewrites needed.

ChangelogBlog·Jan 1

marimo 0.19.0

Refreshed UI, new Developer Panel, pytest fixtures, and more.

FeatureBlog·Nov 1

Announcing our VS Code and Cursor extension

Native marimo experience in VS Code and Cursor.

Viability Score

77/100

Safe Bet

How likely is marimo to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.

momentum

funding runway

website health

wrapper dependency

100

Last calculated: July 2026

How we score →

Key Features

Reactive execution: cells update on dependency change
Git-friendly plain Python (.py) storage
Built-in SQL cells: DuckDB, Postgres, MySQL, SQLite
Interactive UI widgets: sliders, dropdowns, tables, plots
AI autocomplete, chat, and error fixing
marimo pair: connect AI agents to running sessions
Deploy notebooks as WASM HTML or web apps
Run notebooks as Python scripts via CLI
Reproducible deterministic execution, no hidden state
Package requirements embedded in notebook file
VS Code and Cursor extension for IDE integration
MCP support for AI agent connectivity
JupyterHub extension for enterprise deployment
Customizable UI with themes and vim keybindings
Debugging panels and profiler

About marimo

FreemiumIntermediateAPI availableWeb · Desktop · CLI · Plugin

marimo is an open-source Python notebook that reimagines the notebook as a reactive, reproducible Python program rather than a JSON scratchpad. It is designed for data scientists, engineers, and analysts who need to run experiments, build dashboards, and share interactive reports. Unlike traditional notebooks, marimo cells are reactive: changing one cell automatically updates all dependent cells, eliminating hidden state and out-of-order execution. Notebooks are stored as pure .py files, making them git-friendly, reusable as modules, and runnable as scripts or web apps. It includes built-in SQL support with DuckDB, PostgreSQL, etc., interactive widgets, AI-powered editing (Copilot, chat, pair), and supports deployment via WASM, CLI, or as a web app. Developed by a team with backgrounds at Google Brain, Palantir, and CloudKitchens, marimo is used by organizations like Sumble, Bunkerhill, Vodacom, and DNB. It has a thriving open-source community with 21.7k GitHub stars and is backed by CoreWeave. Compared to Jupyter, marimo eliminates hidden state and ensures reproducibility. Against Streamlit, it offers a notebook interface for iterative development with automatic reactivity. For teams, marimo's pure Python format and deterministic execution make collaboration and version control straightforward.

Behind the Verdict

marimo does what Jupyter notebooks should have done from the start: enforce deterministic execution and eliminate hidden state. If you've ever spent hours debugging a notebook that ran fine out of order but broke when cells reran, marimo will feel like a lifeline. The reactive execution model is its killer feature — change a cell and all dependents update automatically, no manual re-run chains. We'd reach for this when reproducibility matters more than flexibility in cell ordering. Where it bites: you lose the free-form, ad-hoc cell execution that some data explorers rely on. Compared to Streamlit, marimo gives you a notebook frontend instead of a script-to-dashboard pipeline — better for iterative analysis, but less suited for production dashboards needing custom components. The AI assistant (pair mode for agents, autocomplete, chat) is a nice add-on but not yet as polished as GitHub Copilot in IDE extensions. The open-source community is active (21.7k GitHub stars), and the backing by CoreWeave suggests staying power. For teams already in the Jupyter ecosystem, migration might require workflow adjustments, especially around extensions. Overall, marimo is a strong choice for data scientists and engineers who prioritize reproducibility and want a notebook that behaves like proper software.

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Use Cases

Build an interactive dashboard for exploring customer churn data with SQL and Python cells.
Create a reproducible research notebook that automatically regenerates figures when parameters change.
Train and evaluate ML models with real-time feedback on hyperparameter tuning via sliders.
Automate weekly reporting by running a marimo notebook as a cron job in production.
Teach programming or data science with notebooks that react to student inputs and provide instant feedback.
Collaborate with AI agents to explore a dataset, letting the agent write and execute cells iteratively.

Models Under the Hood

openai/anyanthropic/anygemini/anyollama/anyopenai-compatible APIs

Limitations

The free open-source version lacks some enterprise features like SSO and dedicated support (available through CoreWeave partnership).
The molab free tier is limited in compute; GPU access is paid.
As a relatively young project, the ecosystem of community extensions and plugins is smaller than Jupyter's.

12-month cost

Project the real annual outlay, including the implied monthly cost when only an annual tier is published.

Plan

Annual total

Free

Over 12 months

Effective monthly

Free

Billed monthly

Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.

Integrations

DuckDBPostgreSQLMySQLSQLitePolarsPandasPyArrowMatplotlibPlotlyanywidgetJupyterHubVS CodeCursorSkyPilotCoreWeave Cloud

Resources & Guides

Resourcemarimo.io
Home · marimo
Helpful link from marimo.io

Frequently Asked Questions

Tools that pair well with marimo

Common stack mates teams adopt alongside marimo, with the specific reason each pairing earns its keep.

Quadratic

AI-native spreadsheet that writes Python/SQL for live data analysis.

Bito

System-wide context layer for AI coding agents across multi-repo projects

Formula Bot

AI data analytics to analyze data 10x faster without code.

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Used marimo? Help shape our editorial sentiment research.

marimo

Freemium

Reactive Python notebook: reproducible, git-friendly, deployable as apps.

By Tanmay Verma, Founder · Last verified 03 Jul 2026

0 views

Added 7d ago

77/100Safe Bet

Visit Website

In short

Compared withvs Versatile vs Geologicai vs Screenplayiq