Is Dataiku only for large enterprises?

While heavily adopted by large enterprises like Toyota, Michelin, and Novartis, Dataiku offers trials and is used by teams of various sizes. However, its full value is realized in complex, multi-team environments needing unified governance and orchestration.

What kind of infrastructure can Dataiku run on?

Dataiku is built for flexibility and can be deployed on all major cloud providers (AWS, Azure, GCP), on-premises, or in hybrid configurations. It supports Kubernetes for orchestration and can connect to a wide range of data sources and compute engines.

How does the LLM Mesh work?

The LLM Mesh is Dataiku's abstraction layer for connecting to and managing various large language models (both open-source and proprietary APIs). It provides a governed interface for prompt management, cost tracking, and security controls, allowing different teams to use LLMs safely within their workflows.

Can I build custom extensions or integrate my own code?

Yes. While Dataiku provides a powerful visual interface, it fully supports coding in Python and R. Developers can create custom recipes, plugins, and web apps to extend platform functionality and integrate existing codebases.

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Dataiku

Q: What is the core difference between Dataiku and other AI platforms?

Dataiku focuses on a 'formula for AI success' combining People, Orchestration, and Governance. It's designed to unify the entire AI lifecycle—from data prep to ML to GenAI agents—in one governed platform for enterprise scale, rather than being a point solution.

Dataiku is an enterprise AI platform designed for the full AI lifecycle, enabling teams to build and govern models, analytics, and generative AI agents. It unifies data science, machine learning, and analytics into a single, collaborative environment, emphasizing orchestration and governance across any infrastructure. Targeted at enterprises, it helps organizations move from isolated projects to scaled, production-ready AI with tools for data prep, model building, and AI agent deployment. Key capabilities include visual and code-based tools for data exploration, building ML models (including LLMs and RAG chatbots), creating AI agents, and enforcing governance for compliance and cost control. The platform supports collaboration between business and technical users, allowing for rapid prototyping and deployment. It is built for scale, with features to manage AI across diverse teams and industries, from financial services to manufacturing.

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Added on

Sep 21, 2026

Monthly Visits

162.8K

Enterprise AI PlatformAI AgentsLLM MeshGovernanceMachine LearningData ScienceAnalytics

Product information

Everything worth knowing about Dataiku

The complete picture — from everyday features to the technical detail builders and IT folks dig for.

Core features

What it actually does

Unified AI System

Connects data, ML, LLMs, and agents as one system across any infrastructure for enterprise AI orchestration.

Agentic AI Building

Build and deploy agents grounded in your data, pipelines, and models to help get work done, not just generate answers.

AI Governance & Control

Apply consistent governance across all AI with unified visibility, cost controls, and audit-ready oversight to mitigate risk.

Modern AI-Enabled Analytics

Move analysts off spreadsheets and legacy desktops with AI-assisted, governed pipelines to deliver trusted insights at scale.

Enterprise ML Scale

Turn isolated models into shared, production-ready ML so teams can build, reuse, and operationalize ML within enterprise standards.

Collaborative Workflow Platform

Enables business and technical teams to work together in shared workflows, eliminating wait times.

Technical capabilities

LLM Mesh

Proprietary layer for connecting and orchestrating multiple LLMs and generative AI capabilities across the enterprise.

Must watch videos

From demo to defensible: governing GxP AI at scale with Good Machine Learning Practice (GMLP)

53:04

Tutorial53:04

From demo to defensible: governing GxP AI at scale with Good Machine Learning Practice (GMLP)

The GMLP Readiness Webinar covers the impact and solutions of AI in the healthcare and life sciences sectors. Led by experts from Dataiku, the session includes an overview of Dataiku's role in these fields, a deep dive into the GMLP solution, and a live demo. To explore more about Dataiku, check out the rest of our content: CHECK OUT DATAIKU: https://bit.ly/36XBlpK Linkedin: https://bit.ly/3lXyRMb Twitter: @dataiku Instagram: @dataiku

[DEMO] Fixing a vibe coded agent's hidden compliance risk

3:46

Tutorial3:46

[DEMO] Fixing a vibe coded agent's hidden compliance risk

If you want to use Cobuild, reach out to your platform and team for Dataiku or contact us at: https://www.dataiku.com/product/reque... AI agents in regulated industries can’t be black boxes. In healthcare, life sciences, and clinical trials, it’s not enough to say an agent worked. Teams need to understand what data it used, what logic it followed, and how it produced its output. In this video, see how Cobuild turns a coding agent for clinical trial site selection into an explainable, auditable pipeline that teams can inspect and govern, without needing to write code. That’s what Cobuild delivers for regulated industries: not just speed, but trust, transparency, and compliance. To explore more about Dataiku, check out the rest of our content: CHECK OUT DATAIKU: https://bit.ly/36XBlpK Linkedin: https://bit.ly/3lXyRMb Twitter: @dataiku Instagram: @dataiku

AI changes jobs. What else has to change?

46:56

Tutorial46:56

AI changes jobs. What else has to change?

As AI takes on more work, the questions shift. Which roles should change? Where does human judgment become more valuable? What does good management look like when work is shared between people and AI? And how do you govern all of this without getting in the way? In this webinar, Dataiku CDO Catalina Herrera and PeopleReign CEO Dan Turchin discuss the implications for roles, managers, talent, governance, and operating models — including what shadow AI can reveal and how to tell whether AI is actually improving the organization, not just making it more efficient. To explore more about Dataiku, check out the rest of our content: CHECK OUT DATAIKU: https://bit.ly/36XBlpK Linkedin: https://bit.ly/3lXyRMb Twitter: @dataiku Instagram: @dataiku

Who uses it

Real use cases, no hype

Enterprise AI Orchestration

Unify disparate data, ML, and LLM pipelines into a single, governed system across any infrastructure.

Agentic AI Deployment

Build and operationalize AI agents grounded in proprietary data, pipelines, and models for real business ROI.

Modern Analytics Migration

Move analysts from spreadsheets and legacy desktops to AI-assisted, governed pipelines for trusted enterprise insights.

Scaled Machine Learning Operations

Break down silos to turn isolated models into shared, production-ready ML for continuous reuse and standardization.

AI Governance & Risk Control

Apply consistent visibility, cost controls, and compliance checks across all AI initiatives, inside and outside the platform.

Generative AI Prototyping

Rapidly build working LLM and GenAI prototypes with a visual interface, as demonstrated by Johnson & Johnson's <2 day hackathons.

What's great

Unified platform for data, ML, and GenAI agents
Strong collaborative workflows for business and technical teams
Robust governance, cost control, and audit capabilities
Scalable from individual models to enterprise-wide ML

Technical strengths

Visual, low-code interface accelerates prototyping and deployment
Built-in LLM Mesh for managed access to various large language models
Supports containerized, hybrid, and on-prem deployment architectures

Where it falls short

Enterprise-level pricing can be prohibitive for smaller teams
Steep learning curve for full platform mastery
Can feel heavyweight for simple, single-use-case projects
Customization may require deep technical knowledge of its plugin system

Technical limitations

Abstracts away some underlying model/framework details
Dependency on Dataiku's orchestration layer for complex pipelines

For technical folks

The deep-dive specs

FAQ

Includes technical Q&A

Dataiku focuses on a 'formula for AI success' combining People, Orchestration, and Governance. It's designed to unify the entire AI lifecycle—from data prep to ML to GenAI agents—in one governed platform for enterprise scale, rather than being a point solution.

Traffic Insights

Monthly Visits

162.8K

October 2026

Growth Rate

vs last month

Dominance

in Automation

Top Country

United States

30% of traffic

Top Countries Breakdown

United States

30%

Canada

10%

Japan

10%

Bangladesh

United Kingdom

Growth Trend

Stable

Traffic has remained stable.

Pricing overview

Freemium Pricing

Dataiku Pricing

Dataiku uses a freemium pricing structure. Real-time rates, custom enterprise quotas, and plan details are managed directly on their official platform.

Check Pricing on Dataiku

Reviews