Datalore vs ML Workspace
Datalore
jetbrains.comML Workspace
mltooling.orgSide by side
| Figure | Datalore | ML Workspace |
|---|---|---|
| Price from | $29.17/mo | Free |
| Free plan | Yes | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | Yes | Yes |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 7 | 1 |
| Spec score · Data Science Platforms | 9.2 (#1) | 8.3 (#7) |
| Deployment | Datalore On-Premises supports Docker Compose or Kubernetes deployment on 64-bit Linux and does not support ARM, macOS, or Windows hosts. | The project provides Docker images and says they can be deployed on Mac, Linux, and Windows; Docker is required. |
| Purpose | Datalore is a collaborative data science platform for exploring, analyzing, visualizing, and sharing data. | ML Workspace is an all-in-one web-based IDE specialized for machine learning and data science. |
| Support | The On-Premises plan includes enterprise-grade customer support and solution architect consultations. | 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
Datalore
- Cloud FreeFree
- Cloud$35 a month
- Cloud FreeFree
- Cloud annual$29.17 a month
- Cloud$35 a month
- Cloud$350.04 a year
- On-PremisesPriced on request
In their own words
Datalore
Datalore is a collaborative data science platform for exploring, analyzing, visualizing and sharing data. Its Jupyter-compatible notebooks provide coding assistance for Python, Kotlin, Scala and R, with native SQL cells that can transfer query results into pandas DataFrames. Teams can edit code together in real time and share notebooks with view or edit access. Notebooks can become data stories or static and interactive reports. Datalore AI can help write Python, SQL and R code, describe findings and fix errors. Database connections include Amazon Redshift, Azure SQL Database, MariaDB, MySQL, Oracle, PostgreSQL and Snowflake; AWS S3 and Google Cloud Storage buckets can be mounted as notebook folders. The free plan includes 120 CPU S machine hours and 10 GB of cloud storage. The Cloud plan costs $35 a month, billed monthly. On-Premises deployment runs on 64-bit Linux using Docker Compose or Kubernetes, but not on ARM, macOS or Windows hosts. JetBrains states that Datalore deployments are SOC 2 Type II certified and GDPR compliant.
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.