8 Popular Alternatives to JupyterHub 2023

JupyterHub is an open-source solution that serves as a platform for data science and machine learning teams.

There are many ways to set up JupyterHub for your team, depending on your security and customization needs. But this can lead to a lot of engineering headache, which may not interest you if you are trying to spin up quickly and securely.

In this list, we’ll share the best alternatives to setting up JupyterHub. This includes various solutions with free tiers and enterprise plans. Some offer strictly a hosted notebook in the cloud, while others feature a full suite of tools beyond the notebook.

1. Saturn Cloud

Saturn Cloud is a data science platform for scalable Python, R, and Julia for teams and individuals. It offers free and enterprise tiers to meet the needs of new data scientists as well as experienced teams.

Without having to switch any tools, Saturn provides a flexible environment where data scientists can launch high-powered notebooks (Jupyter, RStudio, VS Code, and more) in the cloud, quickly use Dask clusters, GPUs, deploy cloud resources to expand their data science capabilities, collaborate throughout an entire project lifecycle, and more.

2. Google Colab

Colab is a hosted Jupyter notebook service that requires no setup to use, while providing access free of charge to computing resources including GPUs. Colab users can choose between standard or “premium” GPUs in Colab. Standard GPUs in Colab are usually Nvidia T4 Tensor Core GPUs. Premium GPUs include Nvidia V100 or A100 Tensor Core GPUs. Learn more here here.

3. Paperspace Gradient

Paperspace Gradient is an end-to-end machine learning platform where individuals and teams can build, train, and deploy Machine Learning models of any size and complexity.

Paperspace offers a free plan with limits to CPU and GPU machines. They also offer paid plans for greater access.

4. Azure ML

Azure Machine Learning (Azure ML) is a cloud-based service for creating and managing machine learning solutions. It’s designed to help data scientists and machine learning engineers leverage their existing data processing and model development skills & frameworks.

Azure ML is not free, but they offer a free trial.

5. Deepnote

Deepnote is a special-purpose notebook for collaboration. It is Jupyter-compatible.

Deepnote has a free tier with limits on features; they also offer an enterprise tier.

6. Noteable

Noteable is a collaborative notebook platform and supports no-code visualization.

Notable offers a free tier and a enterprise tier.

7. CoCalc

CoCalc is a web-based cloud computing and course management platform for computational mathematics.

CoCalc is available with free and enterprise plans.

8. Amazon SageMaker

Amazon SageMaker is a fully managed machine learning service. With SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment.

SageMaker is not free, but they offer a free trial.

Summary

The data science platforms listed can provide a variety of resources, depending on your team’s needs. By comparing these different product offerings, we hope you found this comprehensive coverage of tools helpful to further your decision. If you’d like to contribute to this article, reach out to mel@saturncloud.io

Additional Resources:

• An Intro to Data Science Platforms

• What are Data Science Platforms

• Most Data Science Platforms are a Bad Idea

• Top Computational Biology Platforms

• Top 10 ML Platforms

• What is Dask and how does it work?

• Setting up JupyterHub

• Setting up JupyterHub Securely on AWS

• Setting up HTTPS and SSL for JupyterHub

• Using JupyterHub with a Private Container Registry

• Setting up JupyterHub with Single Sign-on (SSO) on AWS

• List: How to Setup Jupyter Notebooks on EC2

• List: How to Set Up JupyterHub on AWS

• How to Set up JupyterHub Authentication with Azure Active Directory(AD)

• How to Set up JupyterHub on Azure

• Setting up HTTPS and SSL for JupyterHub

• Using JupyterHub with a Private Container Registry

• [Install Jupyterhub in a VPN with AWS](https://saturncloud.io/blog/install-jupyterhub-in-a-vpn-with-aws/)

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About Saturn Cloud

Saturn Cloud is your all-in-one solution for data science & ML development, deployment, and data pipelines in the cloud. Spin up a notebook with 4TB of RAM, add a GPU, connect to a distributed cluster of workers, and more. Request a demo today to learn more.