Dataiku

What it is

Dataiku calls itself "The Platform for AI Success", "powered by people, orchestration, and governance" and "built for enterprise scale". It is an enterprise data science, machine learning and AI platform rather than a point-and-click app maker. The site's own line for it is "Go from raw data to production AI in one place", and it describes giving "business experts and engineers one place to build the models, AI, and agents your business runs on". The reader it targets is an IT or data leader at a large company who wants many builders working inside one governed environment, plus the business analysts and data scientists those leaders manage. The company says it is "Trusted by 1 in 4 of the world's top companies". The home page adds that Dataiku has been named a Leader in the Gartner Magic Quadrant for AI Platforms for Data Science and ML for the fifth consecutive year, in the 2026 edition.

What you build

Dataiku builds three kinds of things, and the home page names all of them. The first is machine learning and data science work: "Transform data into intelligence you own" is the section headline, and "Explore data science and ML" is its link. The second is generative AI. The company's blog describes the platform as including native AutoML, generative AI, AI agents and approval workflows, and the home page mentions the Dataiku LLM Mesh, which one customer story credits for reshaping healthcare market research. The third is agents and the processes they run in. "Put AI to work inside real business processes" is the headline there, and the copy says to "orchestrate models, agents, rules, and human review into governed workflows that run real business processes end to end".

For the chatbot reader, the evidence is a customer result: "Saved 1,600 hours per month with RAG chatbots built in Dataiku". Retrieval-augmented chatbots are something customers build on the platform, and the generative AI tooling behind them sits in the same environment as the ML work. A Johnson & Johnson story on the home page says 650+ of its employees use Dataiku, with "<2 days to build working generative AI and LLM prototypes". A second tile claims "Produced working generative AI prototypes in less than 2 days".

The newest piece is Cobuild, which the site calls "Dataiku's agentic interface": "Explore your data, build governed AI, and oversee the agents you run, all in natural language across the whole platform." In other words you can describe what you want rather than click through every step. Next to it sits Agent Management, listed as "now generally available". It lets you "see every agent running across your platforms, track its value and risk, and manage its lifecycle from one place". A third item, Expert-to-Agent, is described as coordinating "agents, models, data, and human judgment into one system that runs a whole process". The same agent-control idea appears in the section called "Control every agent running across your business", which promises to "track cost, performance, and risk in one place, so you scale the agents that deliver and catch the ones that don't".

Data access is the other half of the build story. The company blog says Dataiku "supports in-database processing across major cloud data platforms", meaning computation happens where the data lives rather than after extraction, and its related posts cover running analytics with Dataiku on Databricks and on Snowflake. The same post says teams can move from no-code to code-based workflows on the same pipelines, so analysts, data scientists and engineers work on one project without handing it off between tools. It adds that Dataiku includes built-in capabilities for model deployment, monitoring and governance, and that its approval workflows help satisfy EU AI Act and sector-specific compliance requirements. The home page's "Orchestration" pillar says you can "run AI where you want", and the "People" pillar says "Business experts build. IT stays in control", with the business experts building "inside the guardrails IT sets". The Orchestration pillar says Dataiku runs on the clouds, data platforms and models you already have, and the Governance pillar says oversight is built into every step, so nothing ships without being tested and signed off.

Visual versus code is worth a look. A thread on the Dataiku Community forum from May 2025 (version 13) shows a user asking which drag-and-drop steps have no no-code equivalent in Dataiku. The user says macros, apps that take input during a run, and reporting parts need code: web apps are "code based" and emails need HTML. A Dataiku moderator replies that feature requests can be filed, and a partner says plugins can add handling for different file formats. So the visual layer is real, but power use leans on code recipes, plugins and web apps.

The evidence

The customer numbers on the home page are the company's own claims, each linked to a story. They include "Saved 1,600 hours per month with RAG chatbots", data-backed insights across 50+ global manufacturing sites and "Saved up to $45 million in unplanned employee attrition costs". Named stories cover Roche (agentic AI for patent workflows), OHRA (insurance and efficiency), Aviva (insurance data, machine learning and AI), Novartis (analytics and AI across the organization) and Perdue Farms. A retail data scientist is quoted calling the platform "intuitive, collaborative" and saying it "streamlines workflows from data prep to model deployment." One quote reads "Dataiku was the key platform to solve the problems related to data sourcing and data preparation."

The company also promotes a Dataiku and Harris Poll survey of 685 CIOs worldwide about the pressure to prove AI value, and runs 575 Lab, which it calls Dataiku's Open Source Office. Industry solutions are listed for banking, life sciences, retail and consumer goods, manufacturing, utilities and energy, and the public sector. The community forum shows 8.6K questions and discussions, and a Dataiku Summit in Paris on 29 September 2026 (the forum lists it in French as 29 septembre 2026).

On pricing, the home page offers "Start trial" and "Request a demo"; the site publishes no plan prices, so cost comes out of a sales conversation. The blog's own planning guidance says a focused deployment for a single team of 50 to 100 users can be production-ready in 8 to 12 weeks, while enterprise-wide rollouts typically take 6 to 12 months.

Who it suits

Dataiku fits a large organization with multiple teams building analytics, ML and generative AI, where one platform has to serve both citizen builders and engineers and IT wants governance, approval steps and a view of every agent. The 50+ manufacturing sites, insurance and healthcare stories on the home page describe that profile. A regulated company that has to show how AI decisions are reviewed will like the approval workflows and the agent registry.

It is less suited to a solo founder, a small team or anyone who wants a quick form, bot or internal app. There is no self-serve entry plan with a listed price, and the forum thread shows non-coders running into code-only parts. Treat it as a platform decision for a company, not a tool you pick up on a weekend.