Google Colab vs ML Workspace
Google Colab
colab.research.google.comML Workspace
mltooling.orgSide by side
| Figure | Google Colab | ML Workspace |
|---|---|---|
| Price from | $8.33/mo | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 9 | 1 |
| Spec score · Data Science Platforms | 9.0 (#2) | 8.3 (#7) |
| Integrations | Users can import data from Google Drive, spreadsheets, GitHub, and other sources, and the site lists Google Cloud Storage, Sheets, and BigQuery resources. | Listed tools and integrations include Git, TensorBoard, Netdata, Jupyter, JupyterLab, and Visual Studio Code. |
| Support | Users can report bugs or ask questions through Help > Send feedback in any Colab notebook. | The maintainers say they cannot provide individual support by email and direct users to public support channels; the page lists [email protected] for other requests. |
Plans
Google Colab
- Colab Pro$8.33 a month
- Colab Pro+$41.66 a month
- ColabFree
- Colab freeFree
- Colab EnterprisePriced on request
- Colab ProPrice not published
- Colab Pro+Price not published
- Colab Pro / Pro+ / Pay As You GoPriced on request
- Pay As You GoPrice not published
In their own words
Google Colab
Google Colab lets users write and run Python in a browser without configuring a local environment, with access to GPUs and notebook sharing. Its hosted Jupyter notebooks combine executable code with rich text, images, HTML, and LaTeX. Data can come from Google Drive, spreadsheets, GitHub, and other sources, with Google Cloud Storage, Sheets, and BigQuery resources listed for integration. Colab AI can generate or transform code, explain Python libraries, suggest fixes, and perform multi-step data-analysis workflows. By default, it does not access Drive files or user secrets, though it can generate code to access them when explicitly requested. Google says generative-AI features collect prompts, related code, outputs, usage information, and feedback; human reviewers may process that data for up to 18 months. Free notebooks can run for at most 12 hours, depending on availability and usage. Pro+ supports continuous execution up to 24 hours when enough compute units are available. Colab also has a Visual Studio Code extension and a beta, allowlist-gated API. Paid plans start at 8.33 USD/user/mo (annual).
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.