Best Data Science Platforms in 2026

Google Colab

Colab is aimed at students, data scientists, AI researchers, educators, and researchers who work primarily with Python notebooks.

Gradient suits people and groups developing or serving machine learning models, from individual engineers and researchers to teams and startups.

Deepnote suits data professionals and teams who want collaborative notebooks, scheduled analysis, or dashboards and apps built from their work.

JupyterLab

JupyterLab suits people doing interactive, exploratory computing, including data science, scientific computing, computational journalism and machine learning.

Anaconda Notebooks

Anaconda Notebooks suits learners and Python or data science users who want a browser-based JupyterLab environment.

ML Workspace suits an individual building machine-learning or data-science projects who can deploy and manage Docker.

KNIME Analytics Platform

The desktop platform suits people who want to build data workflows visually, including those who need to combine visual steps with Python, R, or JavaScript.

Hex

Hex describes Community as suited to hobbyists and small projects, Professional to individuals, Team to companies collaborating on data, and Enterprise to organizations needing advanced security, governance, and support.

DataRobot

DataRobot is aimed at global enterprises, including regulated organizations in manufacturing, public sector, life sciences, insurance, financial services, and other listed sectors.

Studio suits analysts, data scientists, and teams working with data, as well as beginners and experts.

H2O.ai

H2O.ai targets businesses, government entities, nonprofits, and academic institutions.

Saturn Cloud

Saturn Cloud suits teams operating GPU infrastructure, AI workloads, or GPU cloud services.

Azure Machine Learning

Azure Machine Learning suits teams needing web-based forecasting, evaluation, automated learning or model deployment.