Alternatives to Google Colab
Compare Google Colab alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Google Colab in 2026. Compare features, ratings, user reviews, pricing, and more from Google Colab competitors and alternatives in order to make an informed decision for your business.
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1
Google Cloud Platform
Google
Google Cloud is a cloud-based service that allows you to create anything from simple websites to complex applications for businesses of all sizes. New customers get $300 in free credits to run, test, and deploy workloads. All customers can use 25+ products for free, up to monthly usage limits. Use Google's core infrastructure, data analytics & machine learning. Secure and fully featured for all enterprises. Tap into big data to find answers faster and build better products. Grow from prototype to production to planet-scale, without having to think about capacity, reliability or performance. From virtual machines with proven price/performance advantages to a fully managed app development platform. Scalable, resilient, high performance object storage and databases for your applications. State-of-the-art software-defined networking products on Google’s private fiber network. Fully managed data warehousing, batch and stream processing, data exploration, Hadoop/Spark, and messaging. -
2
Vertex AI
Google
Build, deploy, and scale machine learning (ML) models faster, with fully managed ML tools for any use case. Through Vertex AI Workbench, Vertex AI is natively integrated with BigQuery, Dataproc, and Spark. You can use BigQuery ML to create and execute machine learning models in BigQuery using standard SQL queries on existing business intelligence tools and spreadsheets, or you can export datasets from BigQuery directly into Vertex AI Workbench and run your models from there. Use Vertex Data Labeling to generate highly accurate labels for your data collection. Vertex AI Agent Builder enables developers to create and deploy enterprise-grade generative AI applications. It offers both no-code and code-first approaches, allowing users to build AI agents using natural language instructions or by leveraging frameworks like LangChain and LlamaIndex. -
3
Google Cloud BigQuery
Google
BigQuery is a serverless, multicloud data warehouse that simplifies the process of working with all types of data so you can focus on getting valuable business insights quickly. At the core of Google’s data cloud, BigQuery allows you to simplify data integration, cost effectively and securely scale analytics, share rich data experiences with built-in business intelligence, and train and deploy ML models with a simple SQL interface, helping to make your organization’s operations more data-driven. Gemini in BigQuery offers AI-driven tools for assistance and collaboration, such as code suggestions, visual data preparation, and smart recommendations designed to boost efficiency and reduce costs. BigQuery delivers an integrated platform featuring SQL, a notebook, and a natural language-based canvas interface, catering to data professionals with varying coding expertise. This unified workspace streamlines the entire analytics process. -
4
Google AI Studio
Google
Google AI Studio is a unified development platform that helps teams explore, build, and deploy applications using Google’s most advanced AI models, including Gemini 3. It brings text, image, audio, and video models together in one interactive playground. With vibe coding, developers can use natural language to quickly turn ideas into working AI applications. The platform reduces friction by generating functional apps that are ready for deployment with minimal setup. Built-in integrations like Google Search enhance real-world use cases. Google AI Studio also centralizes API key management, usage monitoring, and billing. It offers a fast, intuitive path from prompt to production powered by vibe coding workflows. -
5
RunPod
RunPod
RunPod offers a cloud-based platform designed for running AI workloads, focusing on providing scalable, on-demand GPU resources to accelerate machine learning (ML) model training and inference. With its diverse selection of powerful GPUs like the NVIDIA A100, RTX 3090, and H100, RunPod supports a wide range of AI applications, from deep learning to data processing. The platform is designed to minimize startup time, providing near-instant access to GPU pods, and ensures scalability with autoscaling capabilities for real-time AI model deployment. RunPod also offers serverless functionality, job queuing, and real-time analytics, making it an ideal solution for businesses needing flexible, cost-effective GPU resources without the hassle of managing infrastructure. -
6
Amazon SageMaker
Amazon
Amazon SageMaker is an advanced machine learning service that provides an integrated environment for building, training, and deploying machine learning (ML) models. It combines tools for model development, data processing, and AI capabilities in a unified studio, enabling users to collaborate and work faster. SageMaker supports various data sources, such as Amazon S3 data lakes and Amazon Redshift data warehouses, while ensuring enterprise security and governance through its built-in features. The service also offers tools for generative AI applications, making it easier for users to customize and scale AI use cases. SageMaker’s architecture simplifies the AI lifecycle, from data discovery to model deployment, providing a seamless experience for developers. -
7
Posit
Posit
Posit builds tools that help data scientists work more efficiently, collaborate seamlessly, and share insights securely across their organizations. Its Positron code editor provides the speed of an interactive console combined with the power to build, debug, and deploy data-science workflows in Python and R. Posit’s platform enables teams to scale open-source data science, offering enterprise-ready capabilities for publishing, sharing, and operationalizing applications. Companies rely on Posit’s secure infrastructure to host Shiny apps, dashboards, APIs, and analytical reports with confidence. Whether using open-source packages or cloud-based solutions, Posit supports reproducible, high-quality work at every stage of the data lifecycle. Trusted by millions of users—and more than half of the Fortune 100—Posit empowers professionals across industries to innovate with data. -
8
Vertex AI Notebooks
Google
Vertex AI Notebooks is a fully managed, scalable solution from Google Cloud that accelerates machine learning (ML) development. It provides a seamless, interactive environment for data scientists and developers to explore data, prototype models, and collaborate in real-time. With integration into Google Cloud’s vast data and ML tools, Vertex AI Notebooks supports rapid prototyping, automated workflows, and deployment, making it easier to scale ML operations. The platform’s support for both Colab Enterprise and Vertex AI Workbench ensures a flexible and secure environment for diverse enterprise needs.Starting Price: $10 per GB -
9
Vast.ai
Vast.ai
Vast.ai is the market leader in low-cost cloud GPU rental. Use one simple interface to save 5-6X on GPU compute. Use on-demand rentals for convenience and consistent pricing. Or save a further 50% or more with interruptible instances using spot auction based pricing. Vast has an array of providers that offer different levels of security: from hobbyists up to Tier-4 data centers. Vast.ai helps you find the best pricing for the level of security and reliability you need. Use our command line interface to search the entire marketplace for offers while utilizing scriptable filters and sort options. Launch instances quickly right from the CLI and easily automate your deployment. Save an additional 50% or more by using interruptible instances and auction pricing. The highest bidding instances run; other conflicting instances are stopped.Starting Price: $0.20 per hour -
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Azure Machine Learning
Microsoft
Accelerate the end-to-end machine learning lifecycle with Azure Machine Learning Studio. Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML. Productivity for all skill levels, with code-first and drag-and-drop designer, and automated machine learning. Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete ML lifecycle. Responsible ML capabilities – understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the ML lifecycle with audit trials and datasheets. Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R. -
11
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. -
12
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. -
13
Firebase Studio
Google
Firebase Studio is an AI-powered full-stack development platform designed to accelerate the entire development lifecycle, from backend and frontend building to mobile app creation. It integrates AI agents like Gemini to assist in tasks such as coding, debugging, testing, and documentation. With support for various tech stacks and seamless integration with repositories from GitHub, GitLab, and Bitbucket, Firebase Studio helps developers quickly create, deploy, and monitor apps. The platform is optimized for building and testing full-stack applications, providing built-in web previews and emulators for real-time app visualization. -
14
CoCalc
SageMath
Teaching scientific software online. CoCalc is a virtual online computer lab: it takes away the pain of teaching scientific software. Every student works 100% online – inside their own, isolated workspace. Follow the progress of each student in real-time. At any time you can jump into a file of a student, right where they are working. Use TimeTravel to see each step a student took to get to the solution. Integrated chat rooms allows you to guide students directly where they work or discuss collected files with your teaching assistants. The project's Activity Log records exactly when and by whom a file was accessed. Forget any complicated software setup – everyone is able to start working in seconds! Since everyone works with exactly the same software stack, any inconsistencies between your and your students' environments are eliminated. -
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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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16
Paperspace
DigitalOcean
CORE is a high-performance computing platform built for a range of applications. CORE offers a simple point-and-click interface that makes it simple to get up and running. Run the most demanding applications. CORE offers limitless computing power on demand. Enjoy the benefits of cloud computing without the high cost. CORE for teams includes powerful tools that let you sort, filter, create, and connect users, machines, and networks. It has never been easier to get a birds-eye view of your infrastructure in a single place with an intuitive and effortless GUI. Our simple yet powerful management console makes it easy to do things like adding a VPN or Active Directory integration. Things that used to take days or even weeks can now be done with just a few clicks and even complex network configurations become easy to manage. Paperspace is used by some of the most advanced organizations in the world.Starting Price: $5 per month -
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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 -
18
Lambda
Lambda
Lambda provides high-performance supercomputing infrastructure built specifically for training and deploying advanced AI systems at massive scale. Its Superintelligence Cloud integrates high-density power, liquid cooling, and state-of-the-art NVIDIA GPUs to deliver peak performance for demanding AI workloads. Teams can spin up individual GPU instances, deploy production-ready clusters, or operate full superclusters designed for secure, single-tenant use. Lambda’s architecture emphasizes security and reliability with shared-nothing designs, hardware-level isolation, and SOC 2 Type II compliance. Developers gain access to the world’s most advanced GPUs, including NVIDIA GB300 NVL72, HGX B300, HGX B200, and H200 systems. Whether testing prototypes or training frontier-scale models, Lambda offers the compute foundation required for superintelligence-level performance. -
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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 -
20
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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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. -
22
Saturn Cloud
Saturn Cloud
Saturn Cloud is an AI/ML platform available on every cloud. Data teams and engineers can build, scale, and deploy their AI/ML applications with any stack. Quickly spin up environments to test new ideas, then easily deploy them into production. Scale fast—from proof-of-concept to production-ready applications. Customers include NVIDIA, CFA Institute, Snowflake, Flatiron School, Nestle, and more. Get started for free at: saturncloud.ioStarting Price: $0.005 per GB per hour -
23
Thunder Compute
Thunder Compute
Thunder Compute is a GPU cloud platform built for teams searching for cheap cloud GPUs without sacrificing performance, reliability, or ease of use. Developers, startups, and enterprises use Thunder Compute to launch H100, A100, and RTX A6000 GPU instances for AI training, LLM inference, fine-tuning, deep learning, PyTorch, CUDA, ComfyUI, Stable Diffusion, batch inference, and high-performance GPU workloads. With fast GPU provisioning, transparent pricing, persistent storage, and simple deployment, Thunder Compute makes cloud GPU hosting more accessible and cost-effective than traditional hyperscalers. Whether you need affordable GPUs for machine learning, a GPU server for AI, or a low-cost alternative to expensive GPU cloud providers, Thunder Compute helps you scale quickly with reliable on-demand GPU infrastructure designed for modern AI workloads. Thunder Compute is ideal for startups, ML engineers, and research teams that want cheap cloud GPUs with fast setup and predictable costs.Starting Price: $0.27 per hour -
24
Deepnote
Deepnote
Deepnote is building the best data science notebook for teams. In the notebook, users can connect their data, explore, and analyze it with real-time collaboration and version control. Users can easily share project links with team collaborators, or with end-users to present polished assets. All of this is done through a powerful, browser-based UI that runs in the cloud. We built Deepnote because data scientists don't work alone. Features: - Sharing notebooks and projects via URL - Inviting others to view, comment and collaborate, with version control - Publishing notebooks with visualizations for presentations - Sharing datasets between projects - Set team permissions to decide who can edit vs view code - Full linux terminal access - Code completion - Automatic python package management - Importing from github - PostgreSQL DB connectionStarting Price: Free -
25
CoLab
CoLab
The only tool purpose-built to simplify design reviews for engineering teams building complex products. Use CoLab to collaborate in real time and track feedback automatically for a review process that's painless, and 2x faster. Join the worldwide wave of engineers using CoLab to design better, together. Status quo industry problems don’t need to stay that way. Long review cycles, endless emails, miscommunications over screenshots, mistakes and oversights that turn into expensive changes, tools that don’t integrate, frustrating admin tasks, etc. For engineers, designers, and manufacturers collaborating on innovative projects, CoLab is the go-to hub to easily find, discuss, track, and resolve every last piece of feedback, all securely integrated with your files so that conversations happen in context, pinned directly to the model. While you’re busy innovating, CoLab automatically tracks comments, revision histories, audit trails, reports, meeting minutes, and notifications. -
26
Neo Colab
iamneo.ai
Upgrade your computer science education standards with Neo Colab! Have our AI-driven state-of-the-art platform at your Institution and forget conventional laboratory setup. Routine learning sessions to provide sustainable programming knowledge for the students. Environment for students to have hands-on training with latest frameworks & technologies. Destination to the university journey. And all that the modern industry looks for is a tech-savvy person. Still carrying the burden of invigilation, evaluation and grading? Our AI-driven platform is there to relieve you! Download detailed student metrics with single-click operation and save them for future audits & review at ease. Track students' participation metric, performance standings, and their code stats. Tracking students status with Neo Colab’s unified dashboard is much simpler than the conventional way. You can see live metrics of students with live coding stats. -
27
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 -
28
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 -
29
DeOldify
DeOldify
DeOldify is a state-of-the-art way to colorize black-and-white images. You can try it right now by visiting the free Google Colab notebook for photos or videos. The notebooks are open source and available to all. To see the evolution of DeOldify, check out the GitHub project and archive. For examples of the most cutting-edge work in restoration and colorization, please contact us. The best version of DeOldify is exclusively available on MyHeritage. MyHeritage provides several choices to ensure all your photos look their best in color. Share these vibrant images with your family and friends to delight them, and start your free trial today.Starting Price: Free -
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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 -
31
Anaconda
Anaconda
Empowering the enterprise to do real data science at speed and scale with a full-featured machine learning platform. Spend less time managing tools and infrastructure, so you can focus on building machine learning applications that move your business forward. Anaconda Enterprise takes the headache out of ML operations, puts open-source innovation at your fingertips, and provides the foundation for serious data science and machine learning production without locking you into specific models, templates, or workflows. Software developers and data scientists can work together with AE to build, test, debug, and deploy models using their preferred languages and tools. AE provides access to both notebooks and IDEs so developers and data scientists can work together more efficiently. They can also choose from example projects and preconfigured projects. AE projects are automatically containerized so they can be moved between environments with ease. -
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Zepl
Zepl
Sync, search and manage all the work across your data science team. Zepl’s powerful search lets you discover and reuse models and code. Use Zepl’s enterprise collaboration platform to query data from Snowflake, Athena or Redshift and build your models in Python. Use pivoting and dynamic forms for enhanced interactions with your data using heatmap, radar, and Sankey charts. Zepl creates a new container every time you run your notebook, providing you with the same image each time you run your models. Invite team members to join a shared space and work together in real time or simply leave their comments on a notebook. Use fine-grained access controls to share your work. Allow others have read, edit, and run access as well as enable collaboration and distribution. All notebooks are auto-saved and versioned. You can name, manage and roll back all versions through an easy-to-use interface, and export seamlessly into Github. -
33
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 -
34
Oracle Data Science
Oracle
A data science platform that improves productivity with unparalleled abilities. Build and evaluate higher-quality machine learning (ML) models. Increase business flexibility by putting enterprise-trusted data to work quickly and support data-driven business objectives with easier deployment of ML models. Using cloud-based platforms to discover new business insights. Building a machine learning model is an iterative process. In this ebook, we break down the process and describe how machine learning models are built. Explore notebooks and build or test machine learning algorithms. Try AutoML and see data science results. Build high-quality models faster and easier. Automated machine learning capabilities rapidly examine the data and recommend the optimal data features and best algorithms. Additionally, automated machine learning tunes the model and explains the model’s results. -
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Papira
Papira
Papira is an AI-powered writing assistant designed to streamline and personalize the writing process. It allows users to automate and customize their writing workflow using AI commands, facilitating the creation, editing, and management of documents with Markdown formatting. Users can apply tailored AI commands to generate text variations, fix grammar, and produce summaries. It offers a library of shared templates and the ability to design custom commands, making it adaptable to diverse writing tasks. Papira integrates leading language models like Anthropic, OpenAI, and Perplexity, providing flexibility for various writing styles and needs. It is accessible through a freemium model, with both free and pro plans available, offering expanded features for advanced users. Papira is available as a Google Colab notebook, allowing users to run the tool without needing to understand how to code.Starting Price: $15 per month -
36
Dataiku
Dataiku
Dataiku is an advanced data science and machine learning platform designed to enable teams to build, deploy, and manage AI and analytics projects at scale. It empowers users, from data scientists to business analysts, to collaboratively create data pipelines, develop machine learning models, and prepare data using both visual and coding interfaces. Dataiku supports the entire AI lifecycle, offering tools for data preparation, model training, deployment, and monitoring. The platform also includes integrations for advanced capabilities like generative AI, helping organizations innovate and deploy AI solutions across industries. -
37
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. -
38
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 -
39
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 -
40
Oracle Machine Learning
Oracle
Machine learning uncovers hidden patterns and insights in enterprise data, generating new value for the business. Oracle Machine Learning accelerates the creation and deployment of machine learning models for data scientists using reduced data movement, AutoML technology, and simplified deployment. Increase data scientist and developer productivity and reduce their learning curve with familiar open source-based Apache Zeppelin notebook technology. Notebooks support SQL, PL/SQL, Python, and markdown interpreters for Oracle Autonomous Database so users can work with their language of choice when developing models. A no-code user interface supporting AutoML on Autonomous Database to improve both data scientist productivity and non-expert user access to powerful in-database algorithms for classification and regression. Data scientists gain integrated model deployment from the Oracle Machine Learning AutoML User Interface. -
41
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 -
42
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 -
43
Metaflow
Netflix
Successful data science projects are delivered by data scientists who can build, improve, and operate end-to-end workflows independently, focusing more on data science, less on engineering. Use Metaflow with your favorite data science libraries, such as Tensorflow or SciKit Learn, and write your models in idiomatic Python code with not much new to learn. Metaflow also supports the R language. Metaflow helps you design your workflow, run it at scale, and deploy it to production. It versions and tracks all your experiments and data automatically. It allows you to inspect results easily in notebooks. Metaflow comes packaged with the tutorials, so getting started is easy. You can make copies of all the tutorials in your current directory using the metaflow command line interface. -
44
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. -
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Zerve AI
Zerve AI
Zerve is the agentic data workspace designed for anyone who works with data, from solo analysts, data scientists, business users and teams alike. Zerve brings together exploration, advanced analysis, collaboration, and production deployment into a single AI-native environment, so that important data work doesn’t stall, break, or disappear. Zerve’s AI agents understand the full context of a project and actively help plan, build, debug, and iterate across multi-step analyses. Agents assist with tasks like cleaning and transforming data, identifying issues, and testing approaches, reducing the manual effort that slows teams down. This means working at a higher level of abstraction without being slowed by setup or syntax. Zerve can be used as SaaS, self-hosted, or even on-premise for highly regulated environments. Zerve is used by data professionals in companies such as BBC, QVC, Dun & Bradstreet, Airbus, and many others.Starting Price: $0 -
46
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. -
47
Outerbounds
Outerbounds
Design and develop data-intensive projects with human-friendly, open-source Metaflow. Run, scale, and deploy them reliably on the fully managed Outerbounds platform. One platform for all your ML and data science projects. Access data securely from your existing data warehouses. Compute with a cluster optimized for scale and cost. 24/7 managed orchestration for production workflows. Use results to power any application. Give your data scientists superpowers, approved by your engineers. Outerbounds Platform allows data scientists to develop rapidly, experiment at scale, and deploy to production confidently. All within the outer bounds of policies and processes defined by your engineers, running on your cloud account, fully managed by us. Security is in our DNA, not at the perimeter. The platform adapts to your policies and compliance requirements through multiple layers of security. Centralized auth, a strict permission boundary, and granular task execution roles. -
48
iGenius Crystal
iGenius
Any teams can access key insights independently by just talking — no training or data literacy skills required. Crystal can be tailored to a specific organization’s needs, meaning your teams and crystal can combine to drive actionable insights. Crystal monitors your data 24/7 and can alert you on important changes, ensuring you will get the answers you need, as well as the ones you didn’t know you needed. Available on mobile and desktop, your teams can get immediate insights anywhere without sifting through reports. Bring your use case to life in days, not months, with crystal’s user-friendly setup. Complete with low-code no-code data source connection and setup, you can make more of your existing BI investments and get immediate business value. -
49
Comet
Comet
Manage and optimize models across the entire ML lifecycle, from experiment tracking to monitoring models in production. Achieve your goals faster with the platform built to meet the intense demands of enterprise teams deploying ML at scale. Supports your deployment strategy whether it’s private cloud, on-premise servers, or hybrid. Add two lines of code to your notebook or script and start tracking your experiments. Works wherever you run your code, with any machine learning library, and for any machine learning task. Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance. Monitor your models during every step from training to production. Get alerts when something is amiss, and debug your models to address the issue. Increase productivity, collaboration, and visibility across all teams and stakeholders.Starting Price: $179 per user per month -
50
Teradata Enterprise AgentStack
Teradata
Teradata Enterprise AgentStack is an integrated platform for building, deploying, and governing enterprise-grade autonomous AI agents that connect to trusted data and analytics, helping organizations move from experimentation to production-ready agentic AI with enterprise-level control. It unifies capabilities to support the full agent lifecycle; AgentBuilder accelerates the creation of intelligent agents using no-code and pro-code tools that integrate with Teradata Vantage and open-source frameworks; the Enterprise MCP delivers secure, context-rich access to governed enterprise data and curated prompts for agent intelligence; AgentEngine provides scalable execution of agents with consistent memory and reliability across hybrid environments; and AgentOps centralizes monitoring, governance, compliance, auditability, and policy enforcement so agents operate within defined guardrails.
