Anaconda Notebooks vs ML Workspace
Anaconda Notebooks
anaconda.comML Workspace
mltooling.orgSide by side
| Figure | Anaconda Notebooks | ML Workspace |
|---|---|---|
| Price from | $15/mo | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| Self-hosted | — | Yes |
| Plans published | 8 | 1 |
| Spec score · Data Science Platforms | 8.4 (#6) | 8.3 (#7) |
| Security | Anaconda says it encrypts stored data at the disk and database levels and encrypts application, database, webhook, and API traffic using TLS/HTTPS. | The documentation describes token or basic authentication options and configurable SSL/HTTPS support. |
Plans
Anaconda Notebooks
- FreeFree
- Starter$15 a month
- Business$50 a month
- CustomPrice not published
- Anaconda Core FreeFree
- Anaconda Core Starter$15 a month
- Anaconda Core Business$50 a month
- Anaconda Core CustomPriced on request
In their own words
Anaconda Notebooks
Anaconda Notebooks puts JupyterLab in a cloud environment for writing, editing, and running Python code through an Anaconda Cloud account. Users can start data science and AI projects in a browser with packages and computing resources supplied by the service, without installing Python locally. Two preconfigured environments are available: one with the latest Anaconda Distribution packages and another with packages selected for AI workflows. Notebooks can be shared by URL so others can open a copy, work with it through an Anaconda account, or download it. Outputs can be deployed as web applications using Panel. Anaconda Assistant for Python can analyze tables and suggest ways to work with data, while Data Connectors let users save CSV files on Anaconda Cloud for Excel or notebook access. The Free plan costs nothing and includes 5 GB cloud storage. Starter costs $15 a month, billed monthly, with 10 GB storage per user. Organizations may block notebook sharing, resulting in a 403 error when a user tries to share.
ML Workspace
ML Workspace is a self-deployed, web-based development environment for machine learning and data science. It brings browser-accessible Jupyter, JupyterLab, Visual Studio Code, and a Linux desktop GUI together, with libraries such as TensorFlow, PyTorch, Keras, and scikit-learn in its main image. Git-related tools include Ungit, Jupytext, nbdime, and a Jupyter extension for pushing notebooks. TensorBoard supports training monitoring, while Netdata and Glances cover hardware monitoring. The workspace can provide a remote runtime for Jupyter, VS Code, PyCharm, Colab, and Atom Hydrogen, typically through passwordless SSH, and can be accessed through a browser, SSH, or VNC. Image options include minimal, R, Spark, and GPU variants; the GPU flavor requires compatible Nvidia drivers and supports CUDA 11.2. Docker is required, with images deployable on Mac, Linux, and Windows. The project describes token or basic authentication and configurable SSL/HTTPS. It is designed for one user, and at least 2 CPUs and 500 MB of memory are recommended for stable use. The free plan costs nothing.