JupyterHub vs Lightning AI Studios

JupyterHub gives groups access to computational environments without requiring each person to install and maintain one. It creates a separate Jupyter environment for each user, and starts, manages and proxies that user's notebook server. It can serve interfaces such as Jupyter Notebook, JupyterLab, RStudio and nteract, with authentication configured through pluggable options including OAuth and GitHub. Organizations can run it in the cloud or on their own hardware, including virtual machines or a laptop. The Littlest JupyterHub targets a small, single-server setup, while Zero to JupyterHub with Kubernetes is designed for larger cloud deployments and includes a Helm chart and deployment guide. The project describes JupyterHub as container-friendly and Kubernetes-compatible, with capacity for up to tens of thousands of users. Its REST API supports administration of the Hub, users and services. JupyterHub is open-source software released under a modified BSD license and is free to use. It supports Python, R, Julia and Scala, with ipynb notebooks, and is intended for classrooms, research groups and corporate data science teams.

Lightning AI Studios is a cloud workspace for training models, running inference, building agents and AI apps, and coding collaboratively. Its persistent environments save installed packages, custom Python versions, and downloaded files. Studios offer browser-accessible VS Code and JupyterLab, plus AI copilots for AI and machine-learning work, including CUDA and PyTorch debugging. Teams can collaborate in real time on private projects, use a full-stack runtime, and deploy full-stack applications. Users can connect S3 buckets and EFS volumes through Lightning Drive, import models from Hugging Face and GitHub, or connect local IDEs such as VS Code, Cursor, and PyCharm over SSH. An SDK supports workflow automation. The Free plan is free and includes one active Studio, 50 GB persistent storage, and up to two concurrent GPUs; that Studio requires a restart every four hours. Pro and Teams plans have higher storage and GPU limits. Enterprise offers deployment in a customer VPC using AWS or GCP cloud credits.