Dataiku

Enterprise AI + data science platform.

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About Dataiku

Dataiku works as an orchestration layer that sits on top of the clouds, data platforms and models you already run. Instead of asking you to move everything into its own stack, it says it "runs on the clouds, data platforms, and models you already have, and keeps working when you change them." Inside that layer, you chain together models, agents, business rules and human review steps into governed workflows that run a business process end to end. The LLM Mesh is the piece that routes generative AI work across model providers. Agent Management tracks every agent, wherever it was built, with cost, performance and risk in one view. Cobuild, the newer natural language interface, lets you explore data, build governed AI and oversee agents by typing instead of clicking through pipelines.

The pitch is aimed squarely at large organizations where IT wants control and business teams want speed. Dataiku frames this as "business experts build, IT stays in control," with approvals and testing built into every step so nothing ships without sign-off. The customer proof points are enterprise heavy: Johnson & Johnson has 650+ employees on the platform and reports building working generative AI prototypes in under 2 days. Another customer says it saved 1,600 hours per month with RAG chatbots built in Dataiku, and others include Roche, Aviva and OHRA. Dataiku also says it's been a Leader in the Gartner Magic Quadrant for AI Platforms for Data Science and ML for five years running, and that 1 in 4 of the world's top companies use it.

The honest read: this is a mature, governance-first platform for companies that already have data teams, multiple model vendors and a growing pile of AI agents nobody is tracking. It isn't a lightweight tool you'll pick up for a side project. There's a trial, but the homepage pushes demos hard. It doesn't publish pricing, and the marketing copy is long on outcomes and short on technical specifics like which clouds and models are supported out of the box. If you're a CIO trying to prove AI value and keep agents on the record, it's worth a serious look. If you're a five-person startup, it's probably far more process than you need.

Key features

Governed workflow orchestration

You can combine models, agents, rules and human review into workflows that run real business processes end to end, with approvals built into each step.

LLM Mesh

The LLM Mesh connects generative AI workloads to multiple model providers, and customers cite it in projects like healthcare market research.

Agent Management

Now generally available, Agent Management shows every agent running across your platforms, including ones built outside Dataiku, and tracks its cost, performance, risk and lifecycle in one place.

Cobuild natural language interface

Cobuild is Dataiku's agentic interface that lets you explore data, build governed AI and oversee your agents in plain language across the whole platform.

Expert-to-Agent coordination

Expert-to-Agent ties agents, models, data and human judgment into a single system meant to run a whole process rather than one isolated task.

Data science and ML from raw data to production

The platform covers the path from data sourcing and preparation through model building and deployment, so engineers and business experts work in the same place.

Best for

enterprises running AI agents across several platforms
IT teams that need governance and sign-off before anything ships
organizations mixing multiple LLM providers and clouds
business analysts building models inside IT guardrails

Skip if

skip this if you're a small team that needs transparent, self-serve pricing, since none is published
skip this if you just need a notebook or a single model API, because the governance layers add overhead
skip this if you want deep technical docs before talking to sales, as the site is mostly case studies and outcomes

Pros & cons

Pros

Agent Management tracks agents wherever they were built, which is rare and useful once you have dozens running
Runs on the clouds, data platforms and models you already have instead of forcing a migration
Strong enterprise track record: named a Gartner Magic Quadrant Leader five years in a row, with Johnson & Johnson, Roche and Aviva as customers
Concrete reported results, like generative AI prototypes in under 2 days and 1,600 hours per month saved with RAG chatbots
Built-in approvals and testing mean governance isn't bolted on afterward

Cons

No public pricing on the site, so you'll have to request a demo or start a trial to learn what it costs
The homepage is heavy on case studies and light on specifics, such as which clouds, data platforms and models are supported
Clearly built for large enterprises, with governance and approval steps that will slow down small teams
Agent Management and Cobuild are recent additions, so there's less public track record for them than for the core data science platform

Frequently asked questions

How does Dataiku fit with my existing cloud and data stack?

Dataiku says it runs on the clouds, data platforms and models you already have and keeps working when you change them. That positions it as an orchestration layer, unlike Databricks, which wants to be the data platform itself.

What is the Dataiku LLM Mesh?

It's the layer that connects your generative AI work to different model providers. Customers report using it in projects such as RAG chatbots that saved 1,600 hours per month.

Can Dataiku track agents built in other tools?

Yes. Agent Management, now generally available, is designed to show every agent running across your platforms, wherever it was built, with cost, performance and risk in one view.

How fast can you build a generative AI prototype?

Johnson & Johnson, which has 650+ employees on Dataiku, reports building working generative AI and LLM prototypes in under 2 days. Your timeline will depend on how clean your data and approval process are.

Is Dataiku only for data scientists?

No. The platform is pitched at both business experts and engineers, and Cobuild adds a natural language interface so non-coders can explore data and build governed AI, a lower barrier than code-first tools like Databricks.

How Dataiku compares

Dataiku vs Databricks

Pick Databricks if you want a single data and compute platform for engineering teams, and Dataiku if you need a governed layer that sits across the stack you already own.

Dataiku vs DataRobot

Both target enterprise ML governance, but Dataiku's agent tracking across platforms and its business user tooling give it the edge for teams scaling AI agents.

Dataiku vs Alteryx

Alteryx is the simpler pick for analyst-led data prep and analytics, while Dataiku goes further into production ML, LLM orchestration and agent oversight.

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