Alternatives to marimo
Compare marimo alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to marimo in 2026. Compare features, ratings, user reviews, pricing, and more from marimo competitors and alternatives in order to make an informed decision for your business.
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1
JetBrains DataSpell
JetBrains
Switch between command and editor modes with a single keystroke. Navigate over cells with arrow keys. Use all of the standard Jupyter shortcuts. Enjoy fully interactive outputs – right under the cell. When editing code cells, enjoy smart code completion, on-the-fly error checking and quick-fixes, easy navigation, and much more. Work with local Jupyter notebooks or connect easily to remote Jupyter, JupyterHub, or JupyterLab servers right from the IDE. Run Python scripts or arbitrary expressions interactively in a Python Console. See the outputs and the state of variables in real-time. Split Python scripts into code cells with the #%% separator and run them individually as you would in a Jupyter notebook. Browse DataFrames and visualizations right in place via interactive controls. All popular Python scientific libraries are supported, including Plotly, Bokeh, Altair, ipywidgets, and others.Starting Price: $229 -
2
Positron
Posit PBC
Positron is a next-generation, free, open source available integrated development environment for data science, built to support both Python and R in one unified workflow. It enables data professionals to move from exploration to production by offering interactive consoles, notebook support, variables and plot panes, and built-in previews of apps alongside code, all without needing extensive configuration. The IDE includes AI-assisted tools like the Positron Assistant and Databot agent to help write or refine code, perform exploratory analysis, and accelerate development. It offers features like a dedicated Data Explorer for viewing dataframes, a connections pane for databases, a variables pane, a plot pane, and seamless switch between R and Python with full support for notebooks, scripts, and visual dashboards. With version control, extensions support, and deep integration with other tools in the Posit Software ecosystem.Starting Price: Free -
3
Polars
Polars
Knowing of data wrangling habits, Polars exposes a complete Python API, including the full set of features to manipulate DataFrames using an expression language that will empower you to create readable and performant code. Polars is written in Rust, uncompromising in its choices to provide a feature-complete DataFrame API to the Rust ecosystem. Use it as a DataFrame library or as a query engine backend for your data models. -
4
Bokeh
Bokeh
Bokeh makes it simple to create common plots, but also can handle custom or specialized use-cases. Plots, dashboards, and apps can be published in web pages or Jupyter notebooks. Python has an incredible ecosystem of powerful analytics tools: NumPy, Scipy, Pandas, Dask, Scikit-Learn, OpenCV, and more. With a wide array of widgets, plot tools, and UI events that can trigger real Python callbacks, the Bokeh server is the bridge that lets you connect these tools to rich, interactive visualizations in the browser. Microscopium is a project maintained by researchers at Monash University. It allows researchers to discover new gene or drug functions by exploring large image datasets with Bokeh’s interactive tools. Panel is a tool for polished data presentation that utilizes the Bokeh server. It is created and supported by Anaconda. Panel makes it simple to create custom interactive web apps and dashboards by connecting user-defined widgets to plots, images, tables, or text.Starting Price: Free -
5
runcell.dev
runcell.dev
Runcell is a Jupyter-native AI agent that understands your notebooks, writes code and executes cells so you can focus on insights, offering four AI-powered modes in one high-performance extension: Interactive Learning Mode provides an AI teacher that explains concepts with live code examples, step-by-step algorithm comparisons and real-time visual execution; Autonomous Agent Mode takes full control of your notebook to execute cells, automate complex workflows, reduce manual tasks and handle errors intelligently; Smart Edit Mode acts as a context-aware assistant, delivering intelligent code suggestions, automated optimizations and real-time syntax and logic improvements; and AI-Enhanced Jupyter lets you ask natural-language questions about your code, generate AI-powered solutions and receive smart recommendations for next steps, all seamlessly integrated into the familiar Jupyter interface.Starting Price: $20 per month -
6
PySpark
PySpark
PySpark is an interface for Apache Spark in Python. It not only allows you to write Spark applications using Python APIs, but also provides the PySpark shell for interactively analyzing your data in a distributed environment. PySpark supports most of Spark’s features such as Spark SQL, DataFrame, Streaming, MLlib (Machine Learning) and Spark Core. Spark SQL is a Spark module for structured data processing. It provides a programming abstraction called DataFrame and can also act as distributed SQL query engine. Running on top of Spark, the streaming feature in Apache Spark enables powerful interactive and analytical applications across both streaming and historical data, while inheriting Spark’s ease of use and fault tolerance characteristics. -
7
Count
Count
Count is a fully collaborative and reactive data whiteboard. It brings together all the flexibility and creativity of a whiteboard, with the power of a SQL IDE, and the reactivity of BI notebooks. It makes it easy to break apart complex SQL queries and data models into interconnected cells to better understand the logic. Add sticky notes and visuals to help explain your work to stakeholders. Collaborate with other analysts or stakeholders as you build so you can get feedback faster. Turn any canvas into an interactive report or slideshow presentation.Starting Price: $34 per editor per month -
8
Quadratic
Quadratic
Quadratic enables your team to work together on data analysis to deliver faster results. You already know how to use a spreadsheet, but you’ve never had this much power. Quadratic speaks Formulas and Python (SQL & JavaScript coming soon). Use the language you and your team already know. Single-line formulas are hard to read. In Quadratic you can expand your recipes to as many lines as you need. Quadratic has Python library support built-in. Bring the latest open-source tools directly to your spreadsheet. The last line of code is returned to the spreadsheet. Raw values, 1/2D arrays, and Pandas DataFrames are supported by default. Pull or fetch data from an external API, and it updates automatically in Quadratic's cells. Navigate with ease, zoom out for the big picture, and zoom in to focus on the details. Arrange and navigate your data how it makes sense in your head, not how a tool forces you to do it. -
9
Streamlit
Streamlit
Streamlit. The fastest way to build and share data apps. Turn data scripts into sharable web apps in minutes. All in Python. All for free. No front-end experience required. Streamlit combines three simple ideas. Embrace Python scripting. Build an app in a few lines of code with our magically simple API. Then see it automatically update as you save the source file. Weave in interaction. Adding a widget is the same as declaring a variable. No need to write a backend, define routes, handle HTTP requests, etc. Deploy instantly. Use Streamlit’s sharing platform to effortlessly share, manage, and collaborate on your apps. A minimal framework for powerful apps. Face-GAN explorer. App that uses Shaobo Guan’s TL-GAN project from Insight Data Science, TensorFlow, and NVIDIA's PG-GAN to generate faces that match selected attributes. Real time object detection. An image browser for the Udacity self-driving-car dataset with real-time object detection. -
10
Beaker Notebook
Two Sigma Open Source
BeakerX is a collection of kernels and extensions to the Jupyter interactive computing environment. It provides JVM support, Spark cluster support, polyglot programming, interactive plots, tables, forms, publishing, and more. All of BeakerX’s JVM languages plus Python and JavaScript have APIs for interactive time-series, scatter plots, histograms, heatmaps, and treemaps. The widgets remain interactive in both notebooks saved to disk, and notebooks published to the web. They include unique features for handling many points, nanosecond resolution, zooming, and exporting. BeakerX’s table widget automatically recognizes pandas data frames and allows you to search, sort, drag, filter, format, select, graph, hide, pin, and export to CSV or clipboard. This makes connecting to spreadsheets quickly and easy. BeakerX has a Spark magic with GUIs for configuration, status, progress, and interrupt of Spark jobs. You can either use the GUI or create your own SparkSession with code. -
11
Hex
Hex
Hex brings together the best of notebooks, BI, and docs into a seamless, collaborative UI. Hex is a modern Data Workspace. It makes it easy to connect to data, analyze it in collaborative SQL and Python-powered notebooks, and share work as interactive data apps and stories. Your default landing page in Hex is the Projects page. You can quickly find projects you created, as well as those shared with you and your workspace. The outline provides an easy-to-browse overview of all the cells in a project's Logic View. Every cell in the outline lists the variables it defines, and cells that return a displayed output (chart cells, Input Parameters, markdown cells, etc.) display a preview of that output. You can click any cell in the outline to automatically jump to that position in the logic.Starting Price: $24 per user per month -
12
Nomic Atlas
Nomic AI
Atlas integrates into your workflow by organizing text and embedding datasets into interactive maps for exploration in a web browser. You shouldn’t have to scroll through Excel files, log Dataframes and page through lists to understand your data. Atlas automatically reads, organizes and summarizes your collections of documents surfacing trends and patterns. Atlas’ pre-organized data interface allows you to quickly surface pathologies and dirty data that can jeopardize your AI projects. Label and tag your data while you clean it with immediate sync to your Jupyter Notebook. Vector databases enable powerful applications such as recommendation systems but are notoriously hard to interpret. Atlas stores, visualizes and lets you search through all of your vectors in the same API.Starting Price: $50 per month -
13
R Markdown
RStudio PBC
R Markdown documents are fully reproducible. Use a productive notebook interface to weave together narrative text and code to produce elegantly formatted output. Use multiple languages including R, Python, and SQL. R Markdown supports dozens of static and dynamic output formats including HTML, PDF, MS Word, Beamer, HTML5 slides, Tufte-style handouts, books, dashboards, shiny applications, scientific articles, websites, and more. R Markdown provides an authoring framework for data science. You can use a single R Markdown file to both. When you open the file in the RStudio IDE, it becomes a notebook interface for R. You can run each code chunk by clicking the icon. RStudio executes the code and display the results inline with your file. -
14
NVIDIA RAPIDS
NVIDIA
The RAPIDS suite of software libraries, built on CUDA-X AI, gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. RAPIDS also focuses on common data preparation tasks for analytics and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes. Accelerate your Python data science toolchain with minimal code changes and no new tools to learn. Increase machine learning model accuracy by iterating on models faster and deploying them more frequently. -
15
MLJAR Studio
MLJAR
It's a desktop app with Jupyter Notebook and Python built in, installed with just one click. It includes interactive code snippets and an AI assistant to make coding faster and easier, perfect for data science projects. We manually hand crafted over 100 interactive code recipes that you can use in your Data Science projects. Code recipes detect packages available in the current environment. Install needed modules with 1-click, literally. You can create and interact with all variables available in your Python session. Interactive recipes speed-up your work. AI Assistant has access to your current Python session, variables and modules. Broad context makes it smart. Our AI Assistant was designed to solve data problems with Python programming language. It can help you with plots, data loading, data wrangling, Machine Learning and more. Use AI to quickly solve issues with code, just click Fix button. The AI assistant will analyze the error and propose the solution.Starting Price: $20 per month -
16
JupyterLab
Jupyter
Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data. JupyterLab is flexible, configure and arrange the user interface to support a wide range of workflows in data science, scientific computing, and machine learning. JupyterLab is extensible and modular, write plugins that add new components and integrate with existing ones. The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include, data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. Jupyter supports over 40 programming languages, including Python, R, Julia, and Scala. -
17
Nextflow
Seqera Labs
Data-driven computational pipelines. Nextflow enables scalable and reproducible scientific workflows using software containers. It allows the adaptation of pipelines written in the most common scripting languages. Its fluent DSL simplifies the implementation and deployment of complex parallel and reactive workflows on clouds and clusters. Nextflow is built around the idea that Linux is the lingua franca of data science. Nextflow allows you to write a computational pipeline by making it simpler to put together many different tasks. You may reuse your existing scripts and tools and you don't need to learn a new language or API to start using it. Nextflow supports Docker and Singularity containers technology. This, along with the integration of the GitHub code-sharing platform, allows you to write self-contained pipelines, manage versions, and rapidly reproduce any former configuration. Nextflow provides an abstraction layer between your pipeline's logic and the execution layer.Starting Price: Free -
18
Jovian
Jovian
Start coding instantly with an interactive Jupyter notebook running on the cloud. No installation or setup required. Start with a blank notebook, follow-along with a tutorial or use a starter template. Manage all your projects on Jovian. Just run jovian.commit() to capture snapshots, record versions and generate shareable links for your notebooks. Showcase your best work on your Jovian profile. Feature projects, notebooks, collections, activities and more. Track changes in code, outputs, graphs, tables, logs and more with simple, intutive and visual notebook diffs. Share your work online, or collaborate privately with your team. Let others build upon your experiments & contribute back. Collaborators can discuss and comment on specific parts of your notebooks, with a powerful cell-level commenting inteface. A flexible comparison dashboard lets you sort, filter, archive and do much more to analyze ML experiments & results. -
19
LemonadeJS
Jspreadsheet
Agnostic Micro Reactive JavaScript Library. LemonadeJS is a dependency-free lightweight library featuring an abstract reactive layer and two-way data binding. It enables the creation of modern platform-agnostic components using pure JavaScript, JSX, or TypeScript. -
20
Collimator
Collimator
Collimator is a modeling and simulation platform for hybrid dynamical systems. We allow engineers to design and test complex, mission critical systems in a way that is reliable, secure, fast and intuitive. Our customers are electrical, mechanical and control systems engineers who are using Collimator to increase productivity, improve performance and collaborate more effectively. They do this using our out of the box features including an intuitive block diagram graphical editor, Python blocks to develop custom algorithms, Jupyter notebooks to parametrize and optimize their systems, high performance computing in the cloud and role based access controls. -
21
Edison Analysis
Edison Scientific
Edison Analysis is a next-generation scientific data-analysis agent built by Edison Scientific. It is the analytical engine underpinning their AI Scientist platform, Kosmos, and it’s available both on Edison’s platform and via API. Edison Analysis performs complex scientific data analysis by iteratively building and updating Jupyter notebooks in a dedicated environment; given a dataset plus a prompt, the agent explores, analyzes, and interprets the data to provide comprehensive insights, reports, and visualizations, very much like a human scientist. It supports execution of Python, R, and Bash code, and includes a full suite of common scientific-analysis packages in a Docker environment. Because all work is done within a notebook, the reasoning is fully transparent and auditable; users can inspect exactly how data was manipulated, which parameters were chosen, how conclusions were drawn, and can download the notebook and associated assets at any time.Starting Price: $50 per month -
22
Modelbit
Modelbit
Don't change your day-to-day, works with Jupyter Notebooks and any other Python environment. Simply call modelbi.deploy to deploy your model, and let Modelbit carry it — and all its dependencies — to production. ML models deployed with Modelbit can be called directly from your warehouse as easily as calling a SQL function. They can also be called as a REST endpoint directly from your product. Modelbit is backed by your git repo. GitHub, GitLab, or home grown. Code review. CI/CD pipelines. PRs and merge requests. Bring your whole git workflow to your Python ML models. Modelbit integrates seamlessly with Hex, DeepNote, Noteable and more. Take your model straight from your favorite cloud notebook into production. Sick of VPC configurations and IAM roles? Seamlessly redeploy your SageMaker models to Modelbit. Immediately reap the benefits of Modelbit's platform with the models you've already built. -
23
Daft
Daft
Daft is a framework for ETL, analytics and ML/AI at scale. Its familiar Python dataframe API is built to outperform Spark in performance and ease of use. Daft plugs directly into your ML/AI stack through efficient zero-copy integrations with essential Python libraries such as Pytorch and Ray. It also allows requesting GPUs as a resource for running models. Daft runs locally with a lightweight multithreaded backend. When your local machine is no longer sufficient, it scales seamlessly to run out-of-core on a distributed cluster. Daft can handle User-Defined Functions (UDFs) in columns, allowing you to apply complex expressions and operations to Python objects with the full flexibility required for ML/AI. Daft runs locally with a lightweight multithreaded backend. When your local machine is no longer sufficient, it scales seamlessly to run out-of-core on a distributed cluster. -
24
Apache Spark
Apache Software Foundation
Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources. -
25
Google Colab
Google
Google Colab is a free, hosted Jupyter Notebook service that provides cloud-based environments for machine learning, data science, and educational purposes. It offers no-setup, easy access to computational resources such as GPUs and TPUs, making it ideal for users working with data-intensive projects. Colab allows users to run Python code in an interactive, notebook-style environment, share and collaborate on projects, and access extensive pre-built resources for efficient experimentation and learning. Colab also now offers a Data Science Agent automating analysis, from understanding the data to delivering insights in a working Colab notebook (Sequences shortened. Results for illustrative purposes. Data Science Agent may make mistakes.) -
26
Azure Notebooks
Microsoft
Develop and run code from anywhere with Jupyter notebooks on Azure. Get started for free. Get a better experience with a free Azure Subscription. Perfect for data scientists, developers, students, or anyone. Develop and run code in your browser regardless of industry or skillset. Supporting more languages than any other platform including Python 2, Python 3, R, and F#. Created by Microsoft Azure: Always accessible, always available from any browser, anywhere in the world. -
27
CData Python Connectors
CData Software
CData Python Connectors simplify the way that Python users connect to SaaS, Big Data, NoSQL, and relational data sources. Our Python Connectors offer simple Python database interfaces (DB-API), making it easy to connect with popular tooling like Jupyter Notebook, SQLAlchemy, pandas, Dash, Apache Airflow, petl, and more. CData Python Connectors create a SQL wrapper around APIs and data protocols, simplifying data access from within Python and enabling Python users to easily connect more than 150 SaaS, Big Data, NoSQL, and relational data sources with advanced Python processing. The CData Python Connectors fill a critical gap in Python tooling by providing consistent connectivity with data-centric interfaces to hundreds of different SaaS/Cloud, NoSQL, and Big Data sources. Download a 30-day free trial or learn more at: https://www.cdata.com/python/ -
28
Xq1
Xquantum
The last cron manager you will ever need. Zero infra deployment of human or AI generated Python scripts on schedule. STEPS: 1. BYOC (Bring your own code): If you already have your code ready in VS Code or Jupyter Notebook, simply paste it on Xq1. Or, you can ask ChatGPT to generate your code for you with a simple prompt "Write a python code for ...." and paste it on Xq1 2. Run your code: Run your code on Xq1. This deploys any packages that you have included in your code, creates a container, and runs the container. If the code runs without errors, you are ready to go ahead 3. Name your code and select schedule: Name your code (cron) for better identification once you deploy it. Select your desired schedule or frequency with which you want to run the code 4. Deploy: Press the deploy button. Xq1 will deploy your container for you and schedule it to run in the frequency or schedule that you have chosen. You will be able to track each run in the 'Cron Monitor' UI on Xq1Starting Price: $9 -
29
OpenFang
OpenFang
OpenFang is an open source Agent Operating System built in Rust that provides a unified runtime for building, deploying, and managing autonomous AI agents at production scale. It packages a batteries-included architecture into a single binary, enabling developers to run agents that operate continuously, build knowledge graphs, and report results to a centralized dashboard without constant user prompts. At the core of OpenFang are “Hands,” pre-built autonomous capability packages that execute on schedules and perform tasks such as lead generation, research, browser automation, and social management. It includes dozens of pre-built agents, native tools, and channel adapters that allow agents to function across platforms like Slack, WhatsApp, Discord, and Teams from a single environment. Security is built into the foundation through multiple defense layers such as WASM sandboxing, cryptographic signing, taint tracking, and tamper-evident audit trails.Starting Price: Free -
30
MAIOT
MAIOT
We commoditize production-ready Machine Learning. ZenML, the star MAIOT product, is an extensible, open-source MLOps framework to create reproducible Machine Learning pipelines. ZenML pipelines are built to take experiments from data versioning to a deployed model. The core design is centered around extensible interfaces to accommodate complex pipeline scenarios, while providing a batteries-included, straightforward “happy path” to achieve success in common use-cases without unnecessary boiler-plate code. We want to enable Data Scientists to focus on use-cases, goals and, ultimately, workflows for Machine Learning, not the underlying technologies. As the Machine Learning landscape is evolving fast, in both Software and Hardware, it is our objective to decouple reproducible workflows to productionize Machine Learning from the required tooling, to make the adoption of new technologies as easy as possible. -
31
CZ CELLxGENE Discover
CZ CELLxGENE
Select two custom cell groups based on metadata to find their top differentially expressed genes. Leverage millions of cells from the integrated CZ CELLxGENE corpus for powerful analysis. Execute interactive analyses on a dataset to explore how patterns of gene expression are determined by spatial, environmental, and genetic factors using an interactive speed no-code UI. Understand published datasets or use them as a launchpad to identify new cell sub-types and states. Census provides access to any custom slice of standardized cell data available on CZ CELLxGENE Discover in R and Python. Explore an interactive encyclopedia of 700+ cell types that provides detailed definitions, marker genes, lineage, and relevant datasets in one place. Browse and download hundreds of standardized data collections and 1,000+ datasets characterizing the functionality of healthy mouse and human tissues. -
32
Gurobi Optimizer
Gurobi Optimization
With our powerful algorithms, you can add complexity to your model to better represent the real world, and still solve your model within the available time. Integrate Gurobi into your applications easily, using the languages you know best. Our programming interfaces are designed to be lightweight, modern, and intuitive, to minimize your learning curve while maximizing your productivity. Our Python API includes higher-level modeling constructs that make it easier to build optimization models. Choose from Anaconda Python distributions with pre-built libraries to support application development, Spyder for graphical development, and Jupyter for notebook-style development. -
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Apache Zeppelin
Apache
Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more. IPython interpreter provides comparable user experience like Jupyter Notebook. This release includes Note level dynamic form, note revision comparator and ability to run paragraph sequentially, instead of simultaneous paragraph execution in previous releases. Interpreter lifecycle manager automatically terminate interpreter process on idle timeout. So resources are released when they're not in use. -
34
CVXOPT
CVXOPT
CVXOPT is a free software package for convex optimization based on the Python programming language. It can be used with the interactive Python interpreter, on the command line by executing Python scripts, or integrated in other software via Python extension modules. Its main purpose is to make the development of software for convex optimization applications straightforward by building on Python’s extensive standard library and on the strengths of Python as a high-level programming language. Efficient Python classes for dense and sparse matrices (real and complex), with Python indexing and slicing and overloaded operations for matrix arithmetic. Interfaces to the linear programming solver in GLPK, the semidefinite programming solver in DSDP5, and the linear, quadratic and second-order cone programming solvers in MOSEK.Starting Price: Free -
35
RStudio
Posit
RStudio IDE is a powerful integrated development environment built for data scientists using R and Python; it features a console, syntax-highlighting editor supporting direct code execution, plotting, history management, debugging tools, and workspace controls. The open source edition runs on Windows, Mac, and Linux desktops and includes code completion, smart indentation, Visual Markdown editing, project-based working directories, integrated support for multiple working directories, R help and documentation search, interactive debugging, and extensive tools for package development, all under the AGPL v3 license. While the open version provides core capabilities for coding and data exploration, commercial editions add enterprise-grade features like database/NoSQL connections, priority support, and commercial licensing options. RStudio IDE empowers users to analyze data, build visualizations, develop packages, and produce reproducible workflows in a trusted open-source environment.Starting Price: $1,163 per year -
36
Rio
Rio
Rio is an open source Python framework that enables developers to build modern web and desktop applications entirely in Python. Inspired by frameworks like React and Flutter, Rio introduces a declarative UI model where components are defined as Python data classes with a build() method, allowing for reactive state management and seamless UI updates. It includes over 50 built-in components adhering to Google's Material Design, facilitating the creation of professional-grade interfaces. Rio's layout system is Pythonic and intuitive, calculating each component's natural size before distributing available space, eliminating the need for traditional CSS. Developers can run applications locally or in the browser with the backend powered by FastAPI and communication handled via WebSockets.Starting Price: Free -
37
Daytona
Daytona
Daytona is a cloud-native development runtime that enables developers and AI agents to instantly create, run, and manage isolated sandboxes for any codebase. Each sandbox runs inside a secure microVM with full Linux compatibility, networking, and persistent storage. Daytona provides SDKs in Python and TypeScript, allowing applications to programmatically execute code, run processes, upload files, or spin up environments dynamically. Teams use Daytona to replace complex local setups with reproducible cloud sandboxes that can be started in seconds and accessed through preview URLs, SSH, or APIs. It’s built for automation, observability, and scalability, powering everything from personal development environments to enterprise-grade agent runtimes. -
38
Polyaxon
Polyaxon
A Platform for reproducible and scalable Machine Learning and Deep Learning applications. Learn more about the suite of features and products that underpin today's most innovative platform for managing data science workflows. Polyaxon provides an interactive workspace with notebooks, tensorboards, visualizations,and dashboards. Collaborate with the rest of your team, share and compare experiments and results. Reproducible results with a built-in version control for code and experiments. Deploy Polyaxon in the cloud, on-premises or in hybrid environments, including single laptop, container management platforms, or on Kubernetes. Spin up or down, add more nodes, add more GPUs, and expand storage. -
39
statsmodels
statsmodels
statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and statistical data exploration. An extensive list of result statistics is available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open-source Modified BSD (3-clause) license. statsmodels supports specifying models using R-style formulas and pandas DataFrames. Have a look at dir(results) to see available results. Attributes are described in results.__doc__ and results methods have their own docstrings. You can also use numpy arrays instead of formulas. The easiest way to install statsmodels is to install it as part of the Anaconda distribution, a cross-platform distribution for data analysis and scientific computing. This is the recommended installation method for most users.Starting Price: Free -
40
Layer7 Live API Creator
Broadcom
Shrink the gap from idea to execution. Get APIs to market faster with secure low-code API development and microservice creation. Layer7 Live API Creator (formerly CA Live API Creator) can be used standalone or with Layer7 API Management. Developers can use a visual interface to speed API development and microservice creation, building new data schemas or integrating existing data sources and systems. Business users can create APIs without deep technical knowledge. Increases agility by reducing the time it takes to create data processing systems. Applying reactive logic rules across diverse data sources enables easy business policy and security enforcement. Reactive logic is auto-ordered, chained and executed integration with events and Webhooks, so you can enhance and extend in JavaScript/Java. Enhances data exploration and transaction processing. Layer7 Live API Creator delivers a customizable UI dynamically generated from data schema. -
41
Modelscape
MathWorks
The Modelscape solution enables financial institutions to reduce the complexity of managing the lifecycle of financial models while improving model documentation, transparency, and compliance. By implementing the solution throughout the model lifecycle, you can use templated model workflows, automated documentation, and artifact linking. Scale algorithms, models, and apps both horizontally and vertically. Provide support for enterprise infrastructure, tooling, and languages such as Python, R, SAS, and MATLAB. Track issues across the model lifecycle with full model lineage, issue, and usage reporting. Use the executive dashboard for model data, custom algorithm execution, automated workflows, and web-based access to a comprehensive, auditable inventory of all models and dependencies. Develop, back-test, and document models and methodologies. Improve transparency, reproducibility, and reusability of models. Automatically generate model documentation and reports. -
42
Thoa
Thoa.io
Thoa is a cloud bioinformatics platform that solves the six most expensive problems researchers face daily: environment and dependency conflicts, pipeline management, reproducibility, scaling compute, collaboration, and data sharing. Run Nextflow and Snakemake workflows on managed cloud infrastructure (up to 12TB RAM) with zero DevOps setup. Thoa's AI-assisted debugger resolves environment issues in real time, so pipelines don't crash hours into execution. Every run automatically captures its full execution context: data, software versions, environment, and machine config. Share complete analyses with collaborators in one click. Recipients can view and re-run results without creating an account or mirroring infrastructure. Supports Docker, Conda, Singularity, Python, and R.Starting Price: $35/user/month -
43
MATLAB
The MathWorks
MATLAB® combines a desktop environment tuned for iterative analysis and design processes with a programming language that expresses matrix and array mathematics directly. It includes the Live Editor for creating scripts that combine code, output, and formatted text in an executable notebook. MATLAB toolboxes are professionally developed, rigorously tested, and fully documented. MATLAB apps let you see how different algorithms work with your data. Iterate until you’ve got the results you want, then automatically generate a MATLAB program to reproduce or automate your work. Scale your analyses to run on clusters, GPUs, and clouds with only minor code changes. There’s no need to rewrite your code or learn big data programming and out-of-memory techniques. Automatically convert MATLAB algorithms to C/C++, HDL, and CUDA code to run on your embedded processor or FPGA/ASIC. MATLAB works with Simulink to support Model-Based Design. -
44
Aurelia
Aurelia
Aurelia's standards-based, unobtrusive style makes it the only framework that empowers you to build components using vanilla JavaScript or TypeScript. If you know modern JS and HTML, there's little more to learn to build even the most complex apps. At the core of Aurelia is a high-performance, reactive system, capable of batching DOM updates in a way that leaves other frameworks, and their virtual DOMs, in the dust. Experience consistent, scalable performance, no matter how complex your UI. Aurelia enables powerful reactive binding to any object. By using adaptive techniques Aurelia selects the most efficient way to observe each property in your model and automatically syncs your UI and your state with best-in-class performance. State management, internationalization and validation - all official plugins from the core team. CLI, VS Code plugin, and Chrome debugger - optional tools to enhance development.Starting Price: Free -
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Bind AI
Bind AI
Bind AI is an advanced AI-powered coding assistant platform that supports over 15 AI models, including Claude 4 Sonnet, GPT 4.1, and Gemini 2.5 Pro. It enables users to generate, edit, and execute code across a wide range of programming languages such as Python, Java, C++, JavaScript, and more within a built-in IDE. Bind AI can help create landing pages, backend scripts, SQL queries, and automate repetitive coding tasks. The platform integrates seamlessly with GitHub and Google Drive, allowing code synchronization and collaborative development. Users can preview HTML webpages and run code snippets directly in the AI-powered editor. Bind AI offers a free 3-day trial for new users to experience its capabilities.Starting Price: $18/month -
46
Tellurium
Tellurium
Tellurium is a Python package that knits together a variety of important packages for carrying out simulation studies in systems biology and other disciplines. Tellurium provides an interface to the powerful high-performance lib roadrunner simulation engine. Tellurium allows you to build your models using an easy-to-use human-readable version of SBML called Antimony. Antimony Tutorial. Tellurium supports all the major standards such as SBML, SED-ML, and COMBINE archives. Tellurium can be used via GUI front-ends such as Spyder, PyCharm, or Jupyter Notebooks (including CoLab) with support for advanced productivity and interactive editing features. Installation is via standard pip installation. We also provide a one-click installer for Windows users which provides a complete environment for systems biology modeling. Tellurium relies on open-source contributions from many people.Starting Price: $15.00/month/user -
47
Python RPA
Python RPA
Powerful and affordable RPA platform. Use the flexibility of Python, the convenience of low code, and the potential of AI for intelligent automation. Python RPA is an easy-to-use platform for developing and managing bots in Python. The capabilities of Python make the platform an effective and powerful tool for automating business processes. Enterprise-grade orchestrator for managing Python scripts and low-code projects. Basic Python knowledge is enough to start your automation journey. Stay ahead with instant notifications and a status management board. Uninterrupted flow of process execution, keeping things running smoothly. Ensure secured and managed user access. Keep your credentials secured and ensure activities are being logged. Use any library or framework for creating your project. Develop your Python automation in any open-source Python development environment.Starting Price: $275 per month -
48
Hopsworks
Logical Clocks
Hopsworks is an open-source Enterprise platform for the development and operation of Machine Learning (ML) pipelines at scale, based around the industry’s first Feature Store for ML. You can easily progress from data exploration and model development in Python using Jupyter notebooks and conda to running production quality end-to-end ML pipelines, without having to learn how to manage a Kubernetes cluster. Hopsworks can ingest data from the datasources you use. Whether they are in the cloud, on‑premise, IoT networks, or from your Industry 4.0-solution. Deploy on‑premises on your own hardware or at your preferred cloud provider. Hopsworks will provide the same user experience in the cloud or in the most secure of air‑gapped deployments. Learn how to set up customized alerts in Hopsworks for different events that are triggered as part of the ingestion pipeline.Starting Price: $1 per month -
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MLflow
MLflow
MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components. Record and query experiments: code, data, config, and results. Package data science code in a format to reproduce runs on any platform. Deploy machine learning models in diverse serving environments. Store, annotate, discover, and manage models in a central repository. The MLflow Tracking component is an API and UI for logging parameters, code versions, metrics, and output files when running your machine learning code and for later visualizing the results. MLflow Tracking lets you log and query experiments using Python, REST, R API, and Java API APIs. An MLflow Project is a format for packaging data science code in a reusable and reproducible way, based primarily on conventions. In addition, the Projects component includes an API and command-line tools for running projects. -
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Claude Science
Anthropic
Claude Science is an AI-powered scientific research application that helps researchers perform data analysis, literature review, computational workflows, and manuscript preparation within a single environment. Built on Claude models, the application integrates scientific databases, research tools, electronic lab notebooks, HPC systems, and domain-specific software to support end-to-end research workflows. It manages computational environments across local machines, Linux systems, and high-performance computing clusters while maintaining reproducible records of every analysis. Researchers can generate publication-quality figures, perform complex analyses, and trace every result back to the underlying code, environment, and conversation. Claude Science also supports specialized fields including genomics, proteomics, single-cell biology, structural biology, and cheminformatics through preconfigured scientific capabilities.
