Data science & ML platforms · Dataiku

Dataiku

Collaborative platform spanning data preparation and machine learning, used by both analysts and data scientists.

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Dataiku sits between a data-preparation tool and a full ML platform: it provides a visual flow for cleaning and joining data alongside code notebooks, AutoML, and MLOps features for deploying and monitoring models. Its visual, low-code interface is aimed at business analysts, while Python/R notebooks and full pipeline control serve data scientists on the same project. Dataiku can be installed on a customer's own infrastructure or Kubernetes cluster (self-hosted) or run as Dataiku Cloud, a fully managed SaaS offering. It also includes generative AI tooling (LLM Mesh) for building AI applications on top of enterprise data. Paid plans require a custom quote; a limited free edition exists for individuals and small teams.

At a glance

Vendor Dataiku
Pricing model Quote only
Free tier Yes
Deployment Cloud, Self-hosted
Open source No
Best for Mixed teams of analysts and data scientists collaborating on the same data-to-model workflow.

Pricing

A free edition is available for individuals/small teams; paid editions require a custom quote from sales.

Pricing has not been verified yet — see the vendor's site.

Features

  • Visual data preparation flow
  • Python and R notebooks alongside no-code recipes
  • AutoML and visual ML model building
  • MLOps: deployment, monitoring, and model versioning
  • LLM Mesh for generative AI application building
  • Collaborative project workspace for mixed-skill teams
  • Governance and data lineage tracking

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Alteryx Drag-and-drop data preparation and analytic automation platform that extends into predictive modeling. DataRobot Enterprise AutoML and generative AI platform for building, deploying, and governing predictive models. Domino Data Lab Enterprise MLOps and data science platform for running, governing, and scaling model workflows across teams.

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Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. Self-service BI BI tooling that lets business users build their own reports and explore data without waiting on IT or analytics teams. Data lineage A traceable record of where data came from, how it was transformed, and where it is used downstream. AutoML (automated machine learning) Tooling that automates parts of building a machine learning model, such as feature selection, algorithm choice and hyperparameter tuning. MLOps (machine learning operations) The set of practices for deploying, monitoring and maintaining machine learning models reliably in production, extending DevOps to ML systems.

Related guides

How to choose a data science & ML platform The decisions that matter are who builds the models, how automated the modeling should be, and whether you want a cloud-native platform or a portable one.