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SPECIFICTION
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DataRobot vs ML Workspace

DataRobot

datarobot.com

ML Workspace

mltooling.org

Side by side

FigureDataRobotML Workspace
Price from—Free
Free plan—Yes
Free trialYes—
Open source——
WebYesYes
Windows—Yes
macOS—Yes
LinuxYesYes
APIYes—
Self-hostedYesYes
Plans published11
Spec score · Data Science Platforms7.9 (#10)8.3 (#7)
Development toolsAgent development features include a data registry, tool registry, vector store, code-first workflow tools, and workflow evaluation and comparison.It includes browser-based Jupyter, JupyterLab, Visual Studio Code, and a Linux desktop GUI.
IntegrationsThe platform offers integrations for business applications, LLMs, APIs and frameworks, data platforms, and AI infrastructure.Listed tools and integrations include Git, TensorBoard, Netdata, Jupyter, JupyterLab, and Visual Studio Code.
PurposeDataRobot provides a platform to develop, deliver, and govern AI solutions, including agentic, generative, and predictive AI.ML Workspace is an all-in-one web-based IDE specialized for machine learning and data science.
SecurityDataRobot says it encrypts data in transit and at rest and supports SAML single sign-on, LDAP for Self-Managed customers, and multi-factor authentication.The documentation describes token or basic authentication options and configurable SSL/HTTPS support.

Plans

DataRobot

  • Self-Service SaaS trialFree

DataRobot pricing →

ML Workspace

  • ML WorkspaceFree

ML Workspace pricing →

In their own words

DataRobot

DataRobot is a platform for developing, delivering, and governing agentic, generative, and predictive AI solutions. Its Agent Workforce Platform supports building production agents, deploying them across infrastructure, and governing them organization-wide. Development tools include data and tool registries, a vector store, code-first workflows, and workflow evaluation and comparison. Integrations cover business applications, LLMs, APIs and frameworks, data platforms, and AI infrastructure, with documented connections to AWS, Azure, Google Cloud, and Snowflake. The platform can run on-premise, in a virtual private cloud, or as software as a service. DataRobot says it encrypts data in transit and at rest and supports SAML single sign-on, multifactor authentication, and LDAP for self-managed customers. It lists ISO 27001 and SOC 2 Type II certifications. Pricing is on request, with a 30-day trial. The trial costs nothing, allows up to five team members, and deletes data and assets 30 days after expiration unless the user contacts Sales.

Visit datarobot.com

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.

Visit mltooling.org