18.3
Paperspace Gradient is a machine learning platform for developing, tracking, and collaborating on models.
Free planOpen sourceWebAPIExtension
Best Data Science and Machine Learning Platforms in 2026
Data science and machine learning platforms help with notebooks, model building, automation, deployment and workflow management. The comparison covers pricing, notebook environments, AutoML, GPU compute and pipeline orchestration. Check whether the platform fits your working setup: web, Linux, self-hosted, macOS, Windows or API access may differ. If you plan to work from a Xiaomi, Redmi or POCO phone, confirm…
27.6
Bioconductor is an open-source software project for rigorous, reproducible analysis of biological data.
Free planOpen sourceWebLinuxmacOS
37.6
Google Colab lets users write and execute Python in a browser without configuration, with access to GPUs and notebook sharing.
Free planWebAPIExtension
- 4
Deepnote
Deepnote is an AI workspace for data analysis, exploration, and machine learning.
Free planWebAPIExtensionLinux7.3From $3,920 a month - 5
KNIME Analytics Platform
KNIME Analytics Platform is a free, open-source application from KNIME Analytics Platform, founded in 2006 and headquartered in Zurich, Switzerland.
Free planLinuxmacOSWindows7.1From $19 a month - 6
Anaconda Notebooks
Anaconda Notebooks puts JupyterLab in a cloud workspace for writing, editing, and running Python through an Anaconda Cloud account.
Free planWeb7.0From $15 a month - 7
Saturn Cloud
Saturn Cloud is a platform for operating GPU infrastructure as metered AI services.
Free planWebAPISelf-hosted6.8$0.09 usage-based - 8
Orange Data Mining
Orange Data Mining is free, open-source software for machine learning, data mining, and data visualization.
Free planLinuxmacOSWindows6.3Free plan - 9
Amazon SageMaker Canvas
Free planWeb6.2$1.90 usage-based - 10
H2O Driverless AI
H2O Driverless AI automates machine-learning work such as feature engineering, model building, visualization and interpretation.
WebAPILinuxmacOS6.2 - 11
H2O.ai
H2O.ai offers software for generative and predictive AI, machine learning operations and AI app development.
iOSWebAPISelf-hosted6.2 - 12
DataRobot
DataRobot is a platform for developing, delivering, and governing agentic, generative, and predictive AI solutions.
WebAPILinuxSelf-hosted6.1 - 13
SAS Advanced Analytics
SAS Advanced Analytics refers to SAS Viya, a cloud-native data and AI platform for connecting data, building and governing models, and operationalizing decisions.
APISelf-hosted5.8 - 14
5.6From $0.20 a month - 15
Azure Machine Learning
Azure Machine Learning is a web-based service for machine-learning workflows.
Web5.3 - 16
Databricks Data Intelligence Platform
Databricks Data Intelligence Platform is a web platform listing SQL analytics, ACID transactions, streaming ingestion, and a governance catalog.
Web5.0 - 175.0
- 185.0
- 194.9
- 204.9
- 21
H2O-3
Web4.9 - 224.9
- 23
Apache SystemDS
LinuxmacOSWindows4.7 - 24
Oracle Data Miner
LinuxmacOSWindows4.7 - 254.6
Choosing well
Compare plans and trials
Check the pricing model, free plan status and paid-from price before signing up. A free trial can help you assess a paid service, while freemium plans may limit access to features or usage.
Pricing notes can include a starting price, a trial period or pricing on request. Confirm current terms with the maker.
Match the deployment model
Decide whether you need a web service, Linux or macOS software, Windows support, a self-hosted deployment or an API. The deployment model affects where your notebooks, models and data run.
Check platform support before choosing a tool for a phone or tablet workflow, since the listed platforms do not establish Android availability.
Check the notebook environment
If your work starts in notebooks, compare the notebook environment with the rest of your workflow. Look for the way it handles code, experiments and collaboration based on the platform details.
Pipeline orchestration matters when work moves from exploration to repeatable jobs. Confirm how the platform connects notebook work with scheduled or managed pipelines.
Review automation and compute
AutoML can affect how much model-building work you handle manually. GPU compute is another comparison point when training workloads need accelerated hardware.
If you need a production endpoint, inspect the model-deployment features and the deployment model together.
Consider open source
Open-source code can matter when you need to inspect, adapt or run software yourself. Check the licence and deployment requirements rather than assuming they match a hosted service.
Apache SystemDS is listed with open-source pricing, while other platforms use paid or freemium models.
Questions people ask
Can I start without paying?
The category includes free, freemium and paid pricing models. Check the listing for a free plan or a free trial, then review usage limits and paid terms.
Which platforms can run in a browser?
Web is a listed platform for tools such as Amazon SageMaker AI, Amazon SageMaker Studio, Azure Machine Learning and Cloudera AI Registry. Confirm support for the specific tool.
What should I compare for machine learning workflows?
Compare notebook environment, AutoML, model deployment, GPU compute and pipeline orchestration, along with pricing and deployment model.
Are self-hosted options available?
Self-hosted is one of the deployment platforms represented in this category. Check the individual listing and maker documentation for its deployment requirements.