Alternatives to JetBrains DataSpell
Compare JetBrains DataSpell alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to JetBrains DataSpell in 2026. Compare features, ratings, user reviews, pricing, and more from JetBrains DataSpell competitors and alternatives in order to make an informed decision for your business.
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1
Windsurf Editor
Windsurf
The Windsurf Editor is a free AI-powered IDE and AI coding assistant that accelerates development by providing intelligent code generation and agents in over 70 programming languages and more than 40 IDEs, including VSCode, JetBrains, and Jupyter Notebooks. With Windsurf, developers can write code faster, eliminate repetitive tasks, and stay in the flow state—whether they're working with Python, JavaScript, C++, or any other language. Built on billions of lines of open-source code, Windsurf Editor understands and anticipates your coding needs, offering multiline suggestions, automated unit tests, and even natural language explanations for complex functions. It’s perfect for streamlining code writing, reducing boilerplate, and cutting down the time spent on documentation searches. Trusted by individual developers and Fortune 500 companies alike, Windsurf Editor is your go-to solution for boosting productivity and writing better code. Try Windsurf for free today! -
2
Replit
Replit
Use our free, collaborative, in-browser IDE to code in 50+ languages — without spending a second on setup. Start coding with your favorite language on any platform, OS, and device. Invite your friends, teammates, and colleagues right into your code with Google-docs like editing. Import, run, and collaborate on millions of GitHub repos with 0 manual setup. From Python, to C++, to HTML and CSS, stay in one platform to learn and code in any language you want. The second you create a new repl, it's instantly live and sharable with the world. Learn how to code from 3 million+ passionate programmers, technologists, creatives, and learners of all kinds. Make your team more productive with interactive docs, real-time collaboration, and 0-hassle remote interviewing. Create apps programatically, spin up bots and customize the IDE with plugins to fit your needs.Starting Price: $20 per month -
3
DataGrip
JetBrains
Meet DataGrip, our new database IDE that is tailored to suit the specific needs of professional SQL developers. Allows you to execute queries in different modes and provides a local history that keeps track of all your activity and protects you from losing your work. Lets you jump to any table, view, or procedure by its name via the corresponding action, or directly from its usages in the SQL code. Gives you an extended insight into how your queries work and into the database engine behavior, so you can make your queries more efficient. DataGrip provides context-sensitive code completion, helping you to write SQL code faster. Completion is aware of the tables structure, foreign keys, and even database objects created in code you're editing. DataGrip detects probable bugs in your code and suggests the best options to fix them on the fly. It will immediately let you know about unresolved objects, using keywords as identifiers and always offers a way to fix the problems.Starting Price: $199 per year -
4
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. -
5
marimo
marimo
A reactive notebook for Python — run reproducible experiments, execute as a script, deploy as an app, and version with git. 🚀 batteries-included: replaces jupyter, streamlit, jupytext, ipywidgets, papermill, and more ⚡️ reactive: run a cell, and marimo reactively runs all dependent cells or marks them as stale 🖐️ interactive: bind sliders, tables, plots, and more to Python — no callbacks required 🔬 reproducible: no hidden state, deterministic execution, built-in package management 🏃 executable: execute as a Python script, parametrized by CLI args 🛜 shareable: deploy as an interactive web app or slides, run in the browser via WASM 🛢️ designed for data: query dataframes and databases with SQL, filter and search dataframes 🐍 git-friendly: notebooks are stored as .py files ⌨️ a modern editor: GitHub Copilot, AI assistants, vim keybindings, variable explorer, and moreStarting Price: $0 -
6
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 -
7
JetBrains Datalore
JetBrains
Datalore is a collaborative data science and analytics platform aimed at boosting the whole analytics workflow and making work with data enjoyable for both data scientists and data savvy business teams across the enterprise. Keeping a major focus on data teams workflow, Datalore offers technical-savvy business users the ability to work together with data teams, using no-code or low-code together with the power of Jupyter notebooks. Datalore enables analytical self-service for business users, enabling them to work with data using SQL and no-code cells, build reports and deep dive into data. It offloads the core data team with simple tasks. Datalore enables analysts and data scientists to share results with ML Engineers. You can run your code on powerful CPUs or GPUs and collaborate with your colleagues in real-time.Starting Price: $19.90 per month -
8
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.) -
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JupyterHub
JupyterHub
With JupyterHub you can create a multi-user Hub which spawns, manages, and proxies multiple instances of the single-user Jupyter notebook server. Project Jupyter created JupyterHub to support many users. The Hub can offer notebook servers to a class of students, a corporate data science workgroup, a scientific research project, or a high performance computing group. JupyterHub officially does not support Windows. You may be able to use JupyterHub on Windows if you use a Spawner and Authenticator that work on Windows, but the JupyterHub defaults will not. Bugs reported on Windows will not be accepted, and the test suite will not run on Windows. Small patches that fix minor Windows compatibility issues (such as basic installation) may be accepted, however. For Windows-based systems, we would recommend running JupyterHub in a docker container or Linux VM. -
10
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 -
11
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 -
12
esDynamic
eShard
Maximize your security testing journey, from setting up your bench to analyzing your data processing results, esDynamic saves you valuable time and effort, empowering you to unleash the full potential of your attack workflow. Discover the flexible and comprehensive Python-based platform, perfectly suited for every phase of your security analysis. Customize your research space to meet your unique requirements by effortlessly adding new equipment, integrating tools, and modifying data. Additionally, esDynamic features an extensive collection of materials on complex topics that would typically require extensive research or a team of specialists, granting you instant access to expertise. Say goodbye to scattered data and fragmented knowledge. Welcome a cohesive workspace where your team can effortlessly share data and insights, fostering collaboration and accelerating discoveries. Centralize and solidify your efforts in JupyterLab notebooks to share with your team.Starting Price: Free -
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Jupyter Notebook
Project Jupyter
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. -
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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. -
15
PyCharm
JetBrains
All the Python tools in one place. Save time while PyCharm takes care of the routine. Focus on the bigger things and embrace the keyboard-centric approach to get the most of PyCharm's many productivity features. PyCharm knows everything about your code. Rely on it for intelligent code completion, on-the-fly error checking and quick-fixes, easy project navigation, and much more. Write neat and maintainable code while the IDE helps you keep control of the quality with PEP8 checks, testing assistance, smart refactorings, and a host of inspections. PyCharm is designed by programmers, for programmers, to provide all the tools you need for productive Python development. PyCharm provides smart code completion, code inspections, on-the-fly error highlighting and quick-fixes, along with automated code refactorings and rich navigation capabilities.Starting Price: $199 per user per year -
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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. -
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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. -
18
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 -
19
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 -
20
QuantRocket
QuantRocket
QuantRocket is a Python-based platform for researching, backtesting, and trading quantitative strategies. It provides a JupyterLab environment, offers a suite of data integrations, and supports multiple backtesters: Zipline, the open-source backtester that originally powered Quantopian; Alphalens, an alpha factor analysis library; Moonshot, a vectorized backtester based on pandas; and MoonshotML, a walk-forward machine learning backtester. Built on Docker, QuantRocket can be deployed locally or to the cloud and has an open architecture that is flexible and extensible. -
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
Build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio empowers you to operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. Unite teams, simplify AI lifecycle management and accelerate time to value with an open, flexible multicloud architecture. Automate AI lifecycles with ModelOps pipelines. Speed data science development with AutoAI. Prepare and build models visually and programmatically. Deploy and run models through one-click integration. Promote AI governance with fair, explainable AI. Drive better business outcomes by optimizing decisions. Use open source frameworks like PyTorch, TensorFlow and scikit-learn. Bring together the development tools including popular IDEs, Jupyter notebooks, JupterLab and CLIs — or languages such as Python, R and Scala. IBM Watson Studio helps you build and scale AI with trust and transparency by automating AI lifecycle management.
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23
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. -
24
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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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/ -
26
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. -
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Solara
Widgetti BV
Many Python frameworks can handle basic dashboards but falter with complex ones, often leading teams to split into frontend and backend roles, causing various challenges. Solara is a new web framework that integrates ReactJS principles with Python simplicity. It offers a flexible API for various UI complexities and efficient state management. Solara supports a range of applications, from prototypes to intricate dashboards, and is compatible with platforms like Jupyter, Voilà, and various web servers. It emphasizes code quality, developer accessibility, and robust testing. -
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Kaggle
Kaggle
Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access free GPUs and a huge repository of community published data & code. Inside Kaggle you’ll find all the code & data you need to do your data science work. Use over 19,000 public datasets and 200,000 public notebooks to conquer any analysis in no time. -
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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. -
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Coiled
Coiled
Coiled is enterprise-grade Dask made easy. Coiled manages Dask clusters in your AWS or GCP account, making it the easiest and most secure way to run Dask in production. Coiled manages cloud infrastructure for you, deploying on your AWS or Google Cloud account in minutes. Giving you a rock-solid deployment solution with zero effort. Customize cluster node types to fit your analysis needs. Run Dask in Jupyter Notebooks with real-time dashboards and cluster insights. Create software environments easily with customized dependencies for your Dask analysis. Enjoy enterprise-grade security. Reduce costs with SLAs, user-level management, and auto-termination of clusters. Coiled makes it easy to deploy your cluster on AWS or GCP. You can do it in minutes, without a credit card. Launch code from anywhere, including cloud services like AWS SageMaker, open source solutions, like JupyterHub, or even from the comfort of your very own laptop.Starting Price: $0.05 per CPU hour -
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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. -
32
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 -
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MinusX
MinusX
A Chrome extension that operates your analytics apps for you. MinusX is the fastest way to get insights from data. Interop with MinusX to modify or extend existing notebooks. Select an area and ask questions, or ask for modifications. MinusX works in your existing analytics tools like Jupyter Notebooks, Metabase, Tableau, etc. You can use minusx to create analyses and share results with your team, instantly. We have nuanced privacy controls on MinusX. Any data you share, will be used to train better, more accurate models). We never share your data with third parties. MinusX seamlessly integrates with existing tools. This means that you never have to get out of your workflow to answer questions. Since actions are first-class entities, MinusX can choose the right action for the right context. Currently, we support Claude Sonnet 3.5, GPT-4o and GPT-4o mini. We are also working on a way to let you bring your own models. -
34
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 -
35
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 -
36
Illumina Connected Analytics
Illumina
Store, archive, manage, and collaborate on multi-omic datasets. Illumina Connected Analytics is a secure genomic data platform to operationalize informatics and drive scientific insights. Easily import, build, and edit workflows with tools like CWL and Nextflow. Leverage DRAGEN bioinformatics pipelines. Organize data in a secure workspace and share it globally in a compliant manner. Keep your data in your cloud environment while using our platform. Visualize and interpret your data with a flexible analysis environment, including JupyterLab Notebooks. Aggregate, query, and analyze sample and population data in a scalable data warehouse. Scale analysis operations by building, validating, automating, and deploying informatics pipelines. Reduce the time required to analyze genomic data, when swift results can be a critical factor. Enable comprehensive profiling to identify novel drug targets and drug response biomarkers. Flow data seamlessly from Illumina sequencing systems. -
37
RubyMine
JetBrains
Take advantage of the language specific-aware syntax & error highlighting, code formatting, code completion, and quick documentation. Use smart search to jump to any class, file or symbol, or even any IDE action or tool window. It only takes one click to switch to the declaration, super method, test, usages, implementation, and more. Enjoy super fast navigation in your Rails project with an MVC-based project view and model, class, and gem dependencies diagrams. Follow community best practices with code inspections verifying your code for many types of possible errors and providing on-the-fly improvements with quick-fix options. Automated yet safe refactorings help clean up your code and keep it more maintainable. Rails-aware refactorings help you perform project-wide changes: for example renaming a controller will also rename the helper, views, and tests.Starting Price: $199 per user per year -
38
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 -
39
Protect AI
Palo Alto Networks
Protect AI performs security scans on your ML lifecycle and helps you deliver secure and compliant ML models and AI applications. Enterprises must understand the unique threat surface of their AI & ML systems across the lifecycle and quickly remediate to eliminate risks. Our products provide threat visibility, security testing, and remediation. Jupyter Notebooks are a powerful tool for data scientists to explore data, create models, evaluate experiments, and share results with their peers. The notebooks contain live code, visualizations, data, and text. They introduce security risks and current cybersecurity solutions do not work to evaluate them. NB Defense is free to use, it quickly scans a single notebook or a repository of notebooks for common security issues, identifies problems, and guides your remediation. -
40
StarCoder
BigCode
StarCoder and StarCoderBase are Large Language Models for Code (Code LLMs) trained on permissively licensed data from GitHub, including from 80+ programming languages, Git commits, GitHub issues, and Jupyter notebooks. Similar to LLaMA, we trained a ~15B parameter model for 1 trillion tokens. We fine-tuned StarCoderBase model for 35B Python tokens, resulting in a new model that we call StarCoder. We found that StarCoderBase outperforms existing open Code LLMs on popular programming benchmarks and matches or surpasses closed models such as code-cushman-001 from OpenAI (the original Codex model that powered early versions of GitHub Copilot). With a context length of over 8,000 tokens, the StarCoder models can process more input than any other open LLM, enabling a wide range of interesting applications. For example, by prompting the StarCoder models with a series of dialogues, we enabled them to act as a technical assistant.Starting Price: Free -
41
Bayesforge
Quantum Programming Studio
Bayesforge™ is a Linux machine image that curates the very best open source software for the data scientist who needs advanced analytical tools, as well as for quantum computing and computational mathematics practitioners who seek to work with one of the major QC frameworks. The image combines common machine learning frameworks, such as PyTorch and TensorFlow, with open source software from D-Wave, Rigetti as well as the IBM Quantum Experience and Google's new quantum computing language Cirq, as well as other advanced QC frameworks. For instance our quantum fog modeling framework, and our quantum compiler Qubiter which can cross-compile to all major architectures. All software is made accessible through the Jupyter WebUI which, due to its modular architecture, allows the user to code in Python, R, and Octave. -
42
Google Cloud Datalab
Google
An easy-to-use interactive tool for data exploration, analysis, visualization, and machine learning. Cloud Datalab is a powerful interactive tool created to explore, analyze, transform, and visualize data and build machine learning models on Google Cloud Platform. It runs on Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks. Cloud Datalab is built on Jupyter (formerly IPython), which boasts a thriving ecosystem of modules and a robust knowledge base. Cloud Datalab enables analysis of your data on BigQuery, AI Platform, Compute Engine, and Cloud Storage using Python, SQL, and JavaScript (for BigQuery user-defined functions). Whether you're analyzing megabytes or terabytes, Cloud Datalab has you covered. Query terabytes of data in BigQuery, run local analysis on sampled data, and run training jobs on terabytes of data in AI Platform seamlessly. -
43
JetBrains Fleet
JetBrains
Built from scratch, based on 20 years of experience developing IDEs. JetBrains Fleet uses the IntelliJ code-processing engine, with a distributed IDE architecture and a reimagined UI. We built Fleet to be a fast and lightweight text editor for when you need to quickly browse and edit your code. It starts up in an instant so you can begin working immediately, and it can easily transform into an IDE, with the IntelliJ code-processing engine running separately from the editor itself. Fleet inherits the things that developers love the most from IntelliJ-based IDEs – project and context aware code completion, navigation to definitions and usages, on-the-fly code quality checks, and quick-fixes. Fleet’s architecture is designed to support a range of configurations and workflows. You can simply run Fleet just on your machine, or move some of the processes elsewhere – for example by locating the code processing in the cloud. -
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Vanna.AI
Vanna.AI
Vanna.AI is an AI-powered platform designed to help users interact with their databases by asking questions in natural language. It enables both beginners and experts to quickly obtain insights from large datasets without needing to write complex SQL queries. Users simply ask a question, and Vanna automatically identifies the relevant tables and columns to retrieve the data needed. The platform integrates with popular databases like Snowflake, BigQuery, and Postgres and supports various front-end implementations such as Jupyter Notebooks, Slackbots, and web apps. Vanna's open source model allows for secure, self-hosted deployments and can continuously improve its performance as it learns from the user's interactions. It is ideal for businesses looking to democratize access to data insights and simplify the query process.Starting Price: $25 per month -
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Provision a VM quickly with everything you need to get your deep learning project started on Google Cloud. Deep Learning VM Image makes it easy and fast to instantiate a VM image containing the most popular AI frameworks on a Google Compute Engine instance without worrying about software compatibility. You can launch Compute Engine instances pre-installed with TensorFlow, PyTorch, scikit-learn, and more. You can also easily add Cloud GPU and Cloud TPU support. Deep Learning VM Image supports the most popular and latest machine learning frameworks, like TensorFlow and PyTorch. To accelerate your model training and deployment, Deep Learning VM Images are optimized with the latest NVIDIA® CUDA-X AI libraries and drivers and the Intel® Math Kernel Library. Get started immediately with all the required frameworks, libraries, and drivers pre-installed and tested for compatibility. Deep Learning VM Image delivers a seamless notebook experience with integrated support for JupyterLab.
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NVIDIA Brev
NVIDIA
NVIDIA Brev is a cloud-based platform that provides instant access to fully configured GPU environments optimized for AI and machine learning development. Its Launchables feature offers prebuilt, customizable compute setups that let developers start projects quickly without complex setup or configuration. Users can create Launchables by specifying GPU resources, Docker images, and project files, then share them easily with collaborators. The platform also offers prebuilt Launchables featuring the latest AI frameworks, microservices, and NVIDIA Blueprints to jumpstart development. NVIDIA Brev provides a seamless GPU sandbox with support for CUDA, Python, and Jupyter Lab accessible via browser or CLI. This enables developers to fine-tune, train, and deploy AI models with minimal friction and maximum flexibility.Starting Price: $0.04 per hour -
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KitchenAI
KitchenAI
KitchenAI is a developer-centric framework that streamlines the process of transforming AI Jupyter Notebooks into production-ready APIs. It bridges the gap between AI developers, application developers, and infrastructure developers by providing a fully featured API server with default routes, a command-line interface for quick setup, and an extensible plugin framework. This design enables users to author multiple AI techniques, rapidly test and iterate, and seamlessly build and share their work. For AI developers, KitchenAI manages scalability within familiar environments, converting notebooks into robust applications. Application developers benefit from intuitive SDKs and tools that facilitate the integration of AI through simple APIs, allowing for quick testing to determine the most suitable AI techniques for their applications. Infrastructure developers can integrate with AI tooling.Starting Price: $17 per month -
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Tokern
Tokern
Open source data governance suite for databases and data lakes. Tokern is a simple to use toolkit to collect, organize and analyze data lake's metadata. Run as a command-line app for quick tasks. Run as a service for continuous collection of metadata. Analyze lineage, access control and PII datasets using reporting dashboards or programmatically in Jupyter notebooks. Tokern is an open source data governance suite for databases and data lakes. Improve ROI of your data, comply with regulations like HIPAA, CCPA and GDPR and protect critical data from insider threats with confidence. Centralized metadata management of users, datasets and jobs. Powers other data governance features. Track Column Level Data Lineage for Snowflake, AWS Redshift and BigQuery. Build lineage from query history or ETL scripts. Explore lineage using interactive graphs or programmatically using APIs or SDKs. -
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Gradient
Gradient
Explore a new library or dataset in a notebook. Automate preprocessing, training, or testing with a 2orkflow. Bring your application to life with a deployment. Use notebooks, workflows, and deployments together or independently. Compatible with everything. Gradient supports all major frameworks and libraries. Gradient is powered by Paperspace's world-class GPU instances. Move faster with source control integration. Connect to GitHub to manage all your work & compute resources with git. Launch a GPU-enabled Jupyter Notebook from your browser in seconds. Use any library or framework. Easily invite collaborators or share a public link. A simple cloud workspace that runs on free GPUs. Get started in seconds with a notebook environment that's easy to use and share. Perfect for ML developers. A powerful no-fuss environment with loads of features that just works. Choose a pre-built template or bring your own. Try a free GPU!Starting Price: $8 per month -
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Kubeflow
Kubeflow
The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. Kubeflow provides a custom TensorFlow training job operator that you can use to train your ML model. In particular, Kubeflow's job operator can handle distributed TensorFlow training jobs. Configure the training controller to use CPUs or GPUs and to suit various cluster sizes. Kubeflow includes services to create and manage interactive Jupyter notebooks. You can customize your notebook deployment and your compute resources to suit your data science needs. Experiment with your workflows locally, then deploy them to a cloud when you're ready.
