--- title: JupyterHub Reviews meta_title: 'JupyterHub Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 12 reviews by the users' company size, role or industry to find out how JupyterHub works for a business like yours. aggregate_rating: rating_value: 4.7 review_count: 12 scale: '5' date_modified: '2026-07-12' parent_category: name: IT Infrastructure url: https://www.g2.com/categories/it-infrastructure --- # JupyterHub Reviews **Vendor:** Daniel Rodriguez **Category:** [Operating Systems](https://www.g2.com/categories/operating-system) **Average Rating:** 4.7/5.0 **Total Reviews:** 12 ## About JupyterHub JupyterHub is an open-source platform that enables multiple users to access and work with Jupyter Notebooks in a shared environment. It provides each user with an isolated workspace, allowing them to perform computational tasks without the need for individual installations. Designed for scalability and flexibility, JupyterHub is suitable for educational institutions, research teams, and organizations requiring collaborative data science environments. It can be deployed on various infrastructures, including cloud services and on-premises hardware, facilitating efficient management of resources and user access. Key Features and Functionality: - Multi-User Support: Allows simultaneous access for multiple users, each with their own isolated Jupyter Notebook environment. - Customizable Environments: Supports various kernels and interfaces, including Jupyter Notebook, JupyterLab, RStudio, and more, catering to diverse user needs. - Flexible Authentication: Integrates with multiple authentication protocols such as OAuth and GitHub, enabling secure and adaptable user access management. - Scalability: Deployable on modern container technologies and Kubernetes, JupyterHub can efficiently manage resources for small teams or large-scale infrastructures with thousands of users. - Portability: Being open-source, it can be deployed across various platforms, including cloud providers, virtual machines, or local hardware. Primary Value and User Solutions: JupyterHub addresses the challenge of providing a centralized, collaborative environment for data science and computational tasks. By offering a shared platform with individualized workspaces, it eliminates the complexities associated with setting up and maintaining separate environments for each user. This centralized approach enhances collaboration among teams, streamlines resource management for administrators, and ensures consistency across computational environments. Whether for educational purposes, research collaborations, or enterprise data science initiatives, JupyterHub facilitates efficient, scalable, and secure access to computational resources, empowering users to focus on their work without technical overhead. ## JupyterHub Reviews ### 1. A extension of jupyter notebook for web **Rating:** 4.0/5.0 stars **Reviewed by:** Rutesh R. | Technical Writer, Mid-Market (51-1000 emp.) **Reviewed Date:** November 11, 2022 **What do you like best about JupyterHub?** It expands normal jupyter notebook power to groups of users. It provides users with access to computing environments and resources without requiring them to do installation or maintenance duties **What do you dislike about JupyterHub?** It's not flexible as the normal Jupyter notebook, which runs over a local machine; also, it needs good internet for some parts of processing. Also, its pricing should need to be revised. **What problems is JupyterHub solving and how is that benefiting you?** As it operates on the cloud or on your own hardware and provides a pre-configured ML/data science/python/AI environment to every user in the ecosystem, it saves lot of time and also resources in catering configurations and hardware resources ### 2. Excellent Coding Tool **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in E-Learning | Small-Business (50 or fewer emp.) **Reviewed Date:** November 28, 2022 **What do you like best about JupyterHub?** JupyterHub is the best way to serve Jupyter notebook for multiple users. It can be used in a class of students, a corporate data science group or scientific research group. **What do you dislike about JupyterHub?** It is very hard to test long asynchronous tasks. It runs cell out of order and may not be suitable for all business applications or uses. May require auto data backup regularly. **What problems is JupyterHub solving and how is that benefiting you?** This is great for showcasing your work. You can see both the code and the results. Very easy to host server side, which is useful for security purposes. You can run cell by cell to better get an understanding of what the code does. ### 3. Best Scientific Computing Environment **Rating:** 5.0/5.0 stars **Reviewed by:** Daniel W. | Statistical Programmer(SPII), Small-Business (50 or fewer emp.) **Reviewed Date:** November 18, 2022 **What do you like best about JupyterHub?** JupyterHub is a simple-to-use platform that may be connected to your current infrastructure if you require access to a scientific computing environment. You can plan brand-new deployments to meet your specific requirements and adopt the spawner that best meets those requirements. **What do you dislike about JupyterHub?** It takes a lot of time to complete the installation stage. **What problems is JupyterHub solving and how is that benefiting you?** Creating an environment that is set up and ready for new users using JupyterHub in our organisation. ### 4. Easy to use tool for research and development teams **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Small-Business (50 or fewer emp.) **Reviewed Date:** June 20, 2022 **What do you like best about JupyterHub?** JupyterHub provides a way to collaborate within a team for the use and serving of jupyter notebooks among a group of users. It is vastly used by analysts, data scientists, machine learning professionals and many more. It provides a lot of features including the ability to help groups manage different codes on a server and manage multiple instances. **What do you dislike about JupyterHub?** It may be difficult for new programmers and developers to set up the JupyterHub on their system. It can be difficult to produce full-fledged software using JupyterHub. It is basically good for collaborating and managing code and analysis. **What problems is JupyterHub solving and how is that benefiting you?** I usually work in a team with other analysts and programmers. JupterHub lets us work and cooperate also by providing multiple instances of jupyter notebook which we use for the computations and management of analysis in and for the team. ### 5. Empowering our Research Teams **Rating:** 5.0/5.0 stars **Reviewed by:** Alexandre T. | Graduate research student, Mid-Market (51-1000 emp.) **Reviewed Date:** February 15, 2022 **What do you like best about JupyterHub?** The ability to spawn entire Jupyter Notebook / JupyterLab environments from our custom-built container images is key to providing our researchers access to computational resources. It is possible to employ a series of different technologies to descentralize and distribute work batches: Marathon/Mesos, Yarn/Hadoop, Docker, Kubernetes, etc. If you need access to a scientific computing environment, JupyterHub can easily be attached to your existing infrastructure. New deployments can be planed for your specific needs and adopt the Spawner that best suits your needs. **What do you dislike about JupyterHub?** Some Spawners (modules used to launch instances on your favorite engine) can sometimes be outdated and non-functional on the newest versions of the software, which can be a problem if you're going for a more esoteric deployment (such as using Yarn, for example). Hub-managed services are a powerful tool that integrates well with Jupyter Voila. Still, the lack of a web panel or CLI to configure those and the inability to manage such services without restarting the hub are annoying but not game-breaking by any means. **Recommendations to others considering JupyterHub:** Carefully consider which underlying technologies you'll use to run your sessions. Generally speaking, Docker and systemd are the easiest to set up and maintain, but they do not provide the same flexibility and elasticity you would get from Kubernetes and Mesos. **What problems is JupyterHub solving and how is that benefiting you?** Before migrating to JupyterHub, we gave our researchers remote virtual machines to run computations required for their scientific work. This setup has serious drawbacks: it requires training in a specific toolset (SSH, Git, etc.), doesn't allow for dynamic resource allocation, and hinders our ability to change the underlying hardware without inflicting some downtime. When adopting a solution based on Jupyter Hub running on Apache Mesos, we can now abstract computing resources from machines and users, allowing for elastic cloud operations. Updating environments and changing hardware has now zero downtime, and our researchers don't need to learn a new toolset just to run their computations. ### 6. love jupyterhub for my data science project **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Mid-Market (51-1000 emp.) **Reviewed Date:** November 21, 2022 **What do you like best about JupyterHub?** easy to use, no need to go through the burden of installing softwares, ability to share my work with the team **What do you dislike about JupyterHub?** have not come across any point I dislike about yet **What problems is JupyterHub solving and how is that benefiting you?** it creates an environment ready to use for data scientist and provide computational power to achieve my goal ### 7. Best way to practice python in practical data science **Rating:** 5.0/5.0 stars **Reviewed by:** roopesh a. | Quality analyst, Enterprise (> 1000 emp.) **Reviewed Date:** July 26, 2022 **What do you like best about JupyterHub?** The instance creation is the best than any existing automation environment **What do you dislike about JupyterHub?** It hangs a lot in windows and not a compete VM for script development **What problems is JupyterHub solving and how is that benefiting you?** instance creating and easy user and code maintainace ### 8. Easy environment enhancement server for coding and script sharing **Rating:** 5.0/5.0 stars **Reviewed by:** saravan b. | Team manager, Enterprise (> 1000 emp.) **Reviewed Date:** July 30, 2022 **What do you like best about JupyterHub?** Easy to share resources in a team and contenious integration **What do you dislike about JupyterHub?** Requires more tangible UI for python development like pycharm **What problems is JupyterHub solving and how is that benefiting you?** i used to work some years back on coding and the best with it is it can be used to share and code with teammates and is good for scripting-related work in python and managing instances ### 9. Scaled our custom data science toolkit for the entire team **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Logistics and Supply Chain | Mid-Market (51-1000 emp.) **Reviewed Date:** March 31, 2022 **What do you like best about JupyterHub?** I like how JupyterHub saves us time by skipping the costly setup process of configuring a data science environment for different machines for each of our team members. With JupyterHub, new team members can readily access the tools everyone else in the team uses in a browser. **What do you dislike about JupyterHub?** Setting up JupyterHub. for the first time is quite challenging. But it's typical for most open source projects when self-hosting, so I think that's fine. It's still worth the effort. **What problems is JupyterHub solving and how is that benefiting you?** We used JupyterHub to provide a readily-usable python workspace for our data analysts. Before using JupyterHub, each analyst had to install python and a lot of packages on his/her laptop (some used Mac, some Windows), aside from the in-house python modules for accessing internal systems. This onboarding step is very time-consuming. With JupyterHub, we were able to set up an environment that is already prepared for new users. We just need. to give new team members access and we're good to go. ### 10. Python development made easy **Rating:** 5.0/5.0 stars **Reviewed by:** Prerna S. | Software Development Support Specialist, Mid-Market (51-1000 emp.) **Reviewed Date:** October 16, 2021 **What do you like best about JupyterHub?** JupyterHub is an excellent way to start with Python development, and that makes it easy in terms of running and creating python code. **What do you dislike about JupyterHub?** There is very little to criticize on the capabilities but maybe more support on documentations for development for new users. **What problems is JupyterHub solving and how is that benefiting you?** Scripting without managing dependencies ### 11. Easy to use **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Consumer Services | Mid-Market (51-1000 emp.) **Reviewed Date:** November 25, 2021 **What do you like best about JupyterHub?** JupyterHub is good to develop software in open source and it is compatible with different languages (programming). I like to notebook since it is very easy to use to create and share documents with code.. **What do you dislike about JupyterHub?** So far, I did not find any trouble using Jupyterhub. In future they could have a more extense knowledge center to support new users. But so far the experience is good **What problems is JupyterHub solving and how is that benefiting you?** Script Notebooks to share documents with code Python programing ### 12. My Review on JupyterHub **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Events Services | Small-Business (50 or fewer emp.) **Reviewed Date:** August 24, 2021 **What do you like best about JupyterHub?** JupyterHub is the best tool or more accurate we can say it's the best way to create the multiple instances of the jupyter notebook. When we are working on the project based on the multiple team members then multi- instances are so useful. **What do you dislike about JupyterHub?** The things that are not useful in the jupyter notebook is that it can not use for the good version controlling tools. But multi-instance is possible by using JupyterHub but version controlling is not possible yet. **What problems is JupyterHub solving and how is that benefiting you?** We are creating the prediction model which is use in the my android mobile project in which we are predicting the customer requirements so for this we are working on Jupyter notebook and then Integrate it with JupyterHub. ## JupyterHub Discussions - [How do we integrate the version controlling by using it.](https://www.g2.com/discussions/how-do-we-integrate-the-version-controlling-by-using-it) - 1 comment, 1 upvote - [View JupyterHub pricing details and edition comparison](https://www.g2.com/products/jupyterhub/reviews?section=pricing&secure%5Bexpires_at%5D=2026-07-13+13%3A56%3A17+-0500&secure%5Bsession_id%5D=dca4e0d5-8379-4326-a1ee-29a11beecbb9&secure%5Btoken%5D=85ed0d96aa86d6f060e9794923be31b0668028fbe73fae07460a13d2bcafce27&format=llm_user) ## JupyterHub Features **Memory Management - Operating System** - RAM management **Device Management - Operating System** - I/O management **Backup and Recovery - Operating System** - Data backup **Error Detection - Operating System** - System operations monitoring ## Top JupyterHub Alternatives - [Android](https://www.g2.com/products/android/reviews) - 4.6/5.0 (1,215 reviews) - [Windows 11](https://www.g2.com/products/windows-11/reviews) - 4.5/5.0 (4,113 reviews) - [Ubuntu](https://www.g2.com/products/ubuntu/reviews) - 4.5/5.0 (2,341 reviews)