Dataiku
One workspace where coders and business analysts build the same AI project
Overview
A data-science and generative-AI platform built so Python engineers and non-technical analysts can collaborate on the same pipelines, models and AI apps. Named a Gartner Magic Quadrant Leader for AI/ML platforms five years running.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 13 Jul 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
- Visual flow + coding
- Build data projects and apps
- No production features
- Production deployment
- MLOps + governance
- Designer vs viewer seats
- Cloud or on-prem
What you can produce with Dataiku
- Visual low-code/no-code pipeline builder
- Python, R and SQL coding environments in-platform
- Free Edition (self-hosted, up to 3 collaborators)
- MLOps and model deployment/monitoring
- Generative-AI app building
- Full Python and REST API for orchestration
- AI governance and project collaboration controls
ASEAN Perspective
Dataiku in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
Dataiku's pitch is collaboration across skill levels, and reviewers say it delivers on it. The visual flow lets an analyst prep data and wire a pipeline without code, while an engineer drops into Python, R or SQL on the same project. That shared canvas is the reason mixed technical/business teams pick it over notebook-only tools. Gartner has named it a Leader in the AI-platforms Magic Quadrant five consecutive years, and Peer Insights reviewers give it around 4.7/5. The honest caveats: the Free Edition is real but capped at three users with no deployment, automation or governance, so anything production-grade means an annual, sales-negotiated contract with role-based licensing where builder seats cost far more than viewers. Pricing is opaque and lands in five-to-six figures for most teams. It's a strong fit for enterprises that genuinely need both audiences in one tool; a solo data scientist will find lighter, cheaper options.
What people say
Across G2 and Gartner Peer Insights, Dataiku holds a strong reputation, with a Peer Insights rating near 4.7 out of 5 and roughly 98% of reviewers willing to recommend it as of mid-2026. It was named a Leader in the 2026 Gartner Magic Quadrant for AI Platforms for Data Science and Machine Learning, its fifth consecutive year in that position.
The most-cited strength is collaboration. Reviewers repeatedly describe non-technical colleagues and seasoned data scientists working in the same project, the former using the low-code/no-code visual flow and the latter dropping into Python, R or SQL recipes. Teams credit this for shortening the handoff between analysts and engineers and for making ML development legible to business stakeholders.
Data preparation is the other frequent highlight. Users praise the visual data-wrangling steps and the breadth of built-in processors for cleaning and joining datasets before modeling. The MLOps and deployment features get positive mentions from larger shops managing models in production, and the newer generative-AI app-building tools are described as a natural extension of the existing pipeline model.
The complaints cluster around cost and the learning curve. Pricing is not public; third-party estimates put entry around several thousand dollars a month and six figures annually for enterprise, with role-based licensing that makes "Designer" build seats markedly more expensive than reader or viewer roles, and most plans requiring an annual commitment rather than monthly billing. Buyers dislike the opacity.
The platform's breadth is also a double edge: reviewers new to it report a steep initial ramp before the visual flow "clicks," and some note that heavy customization still pushes you toward code. A few flag that resource-hungry projects can get expensive on compute. Net, the sentiment is that of a capable, well-supported enterprise platform whose main friction points are price transparency and onboarding rather than capability.
Summary of public user & expert reviews, compiled by RECATOOLS.
About this listing
This entry was compiled from publicly available data including Dataiku's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with Dataiku unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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