Dataiku is live · custom pricing · last verified 9 Nov 2026 · viability monitor
Dataiku
Enterprise AI platform for governed AI agents, machine learning, and analytics in one orchestrated system
Pick Dataiku when governance, not model quality, is your bottleneck. Agent Management (GA since September 2026) plus the LLM Mesh gives regulated enterprises a real inventory of every agent's cost and risk, and makes a model swap a config change instead of a rebuild. If your only gap is experiment tracking and a model registry, MLflow costs far less.
Verdict generatedVerified 5d ago · liveness 62/100 · cite: rightaichoice.com/tools/dataiku
- Large enterprises consolidating data science, ML, LLMs, and agents into one governed system
- Regulated finance, insurance, and life sciences teams needing audit-ready AI oversight
- Organizations with agents spreading across departments and no single view of their cost or risk
- Business experts who need to build AI inside guardrails IT controls
- Small startups or solo practitioners wanting a lightweight, low-cost tool
- Teams that only need experiment tracking and a model registry — MLflow covers that cheaper
- Organizations committed to open-source AutoML with no platform contract — H2O fits better
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Skip Dataiku if you are a single data scientist or small team without multiple models, multiple teams, or compliance obligations to reconcile — the governance layer you'd be paying for has nothing to govern.
The free trial excludes Govern and advanced LLM Mesh, so the governance capabilities that justify the platform only surface once you're in a commercial conversation
Dataiku is priced for large regulated enterprises running many teams, models, and agents — the more agents and compliance obligations you have, the better the governance layer pays for itself. Evaluate it against MLflow or H2O, which cost far less if experiment tracking and AutoML are all you need, and note that Dataiku's 14-day trial caps at 2 users and 4 CPUs with Govern and advanced LLM Mesh excluded.
In short
Dataiku — Enterprise AI platform for governed AI agents, machine learning, and analytics in one orchestrated system. Best for Large enterprises consolidating data science, ML, LLMs, and agents into one governed system, Regulated finance, insurance, and life sciences teams needing audit-ready AI oversight, Organizations with agents spreading across departments and no single view of their cost or risk. Pricing is on request (custom quotes).
No head-to-head comparison for Dataiku yet — the alternatives below are the closest matches we list.
What is Dataiku?
Dataiku — Enterprise AI platform for governed AI agents, machine learning, and analytics in one orchestrated system. Best for Large enterprises consolidating data science, ML, LLMs, and agents into one governed system, Regulated finance, insurance, and life sciences teams needing audit-ready AI oversight, Organizations with agents spreading across departments and no single view of their cost or risk. Pricing is on request (custom quotes).
Dataiku is an enterprise AI platform that runs data preparation, machine learning, LLM orchestration, and AI agents as one governed system instead of a stack of disconnected tools. It targets large organizations — banking and financial services, life sciences, retail and CPG, manufacturing, utilities, the public sector — where business experts build AI inside guardrails IT controls. The vendor counts 1 in 4 of the world's top companies as customers and has been named a Leader in the 2026 Gartner Magic Quadrant for AI Platforms for Data Science and ML for the fifth consecutive year. Agent Management is the current headline: generally available since September 2026, it gives you one inventory of every agent running across the business — wherever it was built — with cost, performance, and risk tracked in the same view. Behind it, the LLM Mesh routes work across multiple models and providers so pipelines survive a model swap, and Cobuild provides an agentic natural-language interface for exploring data, building governed AI, and overseeing agents. Expert-to-Agent coordinates agents, models, data, and human judgment into a single end-to-end process. Newer releases widen how teams actually work. Dataiku Headless (October 2026) lets engineers build in their preferred coding agent and then review and ship that work inside Dataiku, which matters for shops that won't give up their own IDE. Governance stays threaded through the lifecycle — oversight, sign-off, semantic observability, and semantic drift detection — so approvals happen in-flow rather than as a separate audit project. Customer results cited on the vendor site run concrete: Toyota saving 1,600 hours per month with RAG chatbots, Johnson & Johnson employees building working generative AI prototypes in under two days, Michelin unlocking insights across 50+ manufacturing sites. Against point tools like H2O or MLflow, Dataiku's claim is breadth — wrapping the whole lifecycle in governance and orchestration rather
Key features
- Build and deploy AI agents grounded in proprietary data, pipelines, and models
- Agent Management tracks cost, performance, and risk for every agent across the business
- Agent Management now generally available (September 2026)
- LLM Mesh connects multiple models and providers across the enterprise
- Multi-model orchestration so pipelines survive a model or provider swap
- Unified governance with compliance, cost, and risk visibility at every layer
- Semantic observability for AI governance and audit readiness
- Detect, measure, and prevent semantic drift in production AI systems
- Human-in-the-loop review steps embedded in agent workflows
- Governed workflows orchestrating models, agents, rules, and human review end to end
- Cobuild: agentic natural-language interface across the whole platform
- Expert-to-Agent coordinates agents, models, data, and human judgment in one process
- Dataiku Headless: build in your preferred coding agent, review and ship in Dataiku
- RAG chatbots for customer and employee use cases
- Machine learning at scale with shared, production-ready model reuse
Behind the verdict
The buying question with Dataiku is rarely "can it build a model?" Almost everything can. It's whether you need one system that holds data prep, ML, LLMs, and agents under a single governance layer — and whether you'll use enough of that surface to justify the platform commitment. We'd reach for it when agents have started spreading across departments and nobody can say what any of them costs or what they touch. Agent Management is the strongest reason to look right now: it inventories agents regardless of where they were originally built, which is the problem most large enterprises actually wake up with. Dataiku Headless is the update worth flagging if your engineers resisted the platform. Building inside a familiar coding agent and reviewing and shipping in Dataiku removes the usual objection that adopting the platform means abandoning your toolchain. Where it bites: this is a platform, not a tool, and platforms need owners. Expect real work on governance policy design, role definitions, and internal enablement before the oversight features pay off. Teams without that capacity will find the surface area heavy. The closest alternative depends on your actual gap. If you need experiment tracking and a model registry, MLflow does that for a fraction of the cost and onboarding. If you're committed to open-source AutoML, H2O fits better. Dataiku competes when the problem is orchestration and audit across many models, agents, and business builders — not a single stage. On price, the vendor page offers a trial and a demo request, and we have no confirmed tier list to quote here. Budget for an enterprise contract and a ramp period, and size the pilot around one governed business process rather than a broad rollout.
Limitations
- Implementation is heavy: extracting value from the governance layer means configuring it, which requires dedicated resources for setup and ongoing administration.
- The platform is enterprise-focused, so simple data science tasks are over-served by its breadth.
- The LLM Mesh and governance depth also assume you already have multiple teams, multiple models, or compliance obligations to reconcile — without that complexity, point tools like MLflow or H2O do the same job for less.
- Users still need a solid grasp of data engineering and ML concepts to get full use out of Cobuild and the agentic tooling; natural language lowers the entry barrier but does not remove it.
- The free trial is capped at a space with 2 users, 1 API service on the API node, 4 CPUs, and 32GiB of elastic compute, so it evaluates the platform rather than proving production load, and Govern plus advanced LLM Mesh are excluded from it.
as of 2026-10-21
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Is Dataiku still active?
Dataiku is live · custom pricing · last verified 9 Nov 2026 · viability monitor.
Viability score
How well maintained and how widely used is Dataiku? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: November 2026
How we score →What's new in Dataiku
Checked 12 days agoAcross the latest 5 updates: 1 feature update, 1 launch and 3 news mentions.
Vestas uses AI agents to streamline wind turbine software support with Dataiku
Dataiku published a customer story on Vestas using AI agents to streamline wind turbine software support.
Dataiku Headless: build in your favorite coding agent, review and ship in Dataiku
Dataiku introduced Headless, which lets teams build in their preferred coding agent and review and ship work inside Dataiku.
Process mining as a starting point for operational improvement
Dataiku published a post on using process mining as a starting point for operational improvement.
AW Rostamani Group scales enterprise AI through agentic intelligence and governance
Dataiku published a customer story on AW Rostamani Group scaling enterprise AI with agentic intelligence and governance.
Discover Dataiku Agent Management: every agent on the record
Dataiku announced Agent Management, a product area for tracking and governing AI agents across the enterprise.
What do users say about Dataiku?
What people actually say about Dataiku — is it worth it?
Our community sentiment research written Jul 26, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about Dataiku — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 9 Nov 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified Dataiku 24 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed pricing page
- — changed summary, who it suits, description, our verdict, features
- — changed pricing page
- — changed our verdict, description, summary, who should skip it, features
- — changed pricing page
- — changed setup time, description, our verdict, features, hidden costs, limitations, switching in, switching out, pricing fit, who should skip it, use cases
Showing the 6 most recent of 24 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Dataiku cost in 2026?
Dataiku: Pricing is on request (custom quotes). Pricing last verified 9 Nov 2026.
Where the pricing makes sense
The company stage and team size where Dataiku's pricing actually pencils out — and where peers do it cheaper.
Dataiku is priced for large regulated enterprises running many teams, models, and agents — the more agents and compliance obligations you have, the better the governance layer pays for itself. Evaluate it against MLflow or H2O, which cost far less if experiment tracking and AutoML are all you need, and note that Dataiku's 14-day trial caps at 2 users and 4 CPUs with Govern and advanced LLM Mesh excluded.
Has Dataiku's pricing changed?
No plan price for Dataiku has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Dataiku?
Pick Dataiku if you are Large enterprises consolidating data science, ML, LLMs, and agents into one governed system, Regulated finance, insurance, and life sciences teams needing audit-ready AI oversight, Organizations with agents spreading across departments and no single view of their cost or risk, Business experts who need to build AI inside guardrails IT controls.
Look elsewhere if you are Small startups or solo practitioners wanting a lightweight, low-cost tool, Teams that only need experiment tracking and a model registry — MLflow covers that cheaper, Organizations committed to open-source AutoML with no platform contract — H2O fits better, Shops with a single data scientist and no compliance or cost-oversight requirements.
Skip it if Skip Dataiku if you are a single data scientist or small team without multiple models, multiple teams, or compliance obligations to reconcile — the governance layer you'd be paying for has nothing to govern.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Dataiku actually fits — and what changes day-one when you adopt it.
Agents have been built in three departments and nobody can say what they cost or whether they're working. You connect Agent Management and pull every agent onto one record covering cost, performance, and risk.
Outcome: A single inventory replaces three departmental spreadsheets, and you can scale the agents that deliver while retiring the ones that don't.
You need predictive maintenance models and a chatbot over equipment documentation. You build both in Dataiku, use the agentic system archetypes mapped to manufacturing operations, and route LLM calls through the LLM Mesh.
Outcome: Models and RAG chatbots run in one governed workflow, and swapping an underlying model doesn't force a rebuild — the pattern Michelin applied across 50+ sites.
You open Cobuild, ask questions in natural language against governed datasets, and turn a recurring analysis into a shared agent that your team can reuse.
Outcome: You ship a working governed agent inside the guardrails IT set, without a ticket to the data team — the pattern behind Johnson & Johnson's 650+ prototype builders.
Use cases
- Give IT a single record of every AI agent running across departments, including agents built outside Dataiku
- Build a customer service chatbot grounded in enterprise knowledge bases using RAG
- Automate data preparation pipelines for financial reporting with governance
- Develop predictive maintenance models for manufacturing equipment
- Implement a governed self-service analytics hub for business teams
- Orchestrate multi-model LLM workflows across different cloud providers without rewriting pipelines on a model swap
- Modernize legacy spreadsheet analytics into AI-assisted, governed pipelines
- Turn one analyst's expertise into a team-wide AI agent, as Novo Nordisk did
Setup time & first value
How long it actually takes to get something useful out of Dataiku — broken out by persona, not the marketing-page minute.
The managed Dataiku Cloud trial provisions in under two minutes, no installation or credit card required, and you can start exploring data immediately. A Free Edition install takes longer but runs locally on Mac, Linux, or Windows. Reaching real production value — governance configured, agents on the record, business experts onboarded — is a multi-week implementation that needs dedicated admin
Switching to or from Dataiku
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From MLflow: keep existing experiment history and registered models, then rebuild pipelines as Dataiku recipes so governance covers them from day one
- →From H2O: export trained models and re-register them inside Dataiku so they sit in the same governed workflow as your LLM and agent work
- →From spreadsheets: import source files as datasets and replace manual steps with Dataiku recipes, as in the Perdue Farms 'data chaos to clarity' migration
- →From a single department's point solution: consolidate agents and models into Agent Management so cost and risk live on one record
- ↗To MLflow: if all you need is experiment tracking and a model registry, MLflow covers that cheaper and Dataiku's governance layer has nothing to govern
- ↗To H2O: teams committed to open-source AutoML with no platform contract fit H2O better
- ↗To a point LLM gateway: if you only need model routing and none of the agent oversight, a narrower gateway covers that slice
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Dataiku”, and we withheld 6: 6 could not be judged, because “Dataiku” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Dataiku.
Official links
What are the best Dataiku alternatives?
The closest alternatives to Dataiku we list are Databricks AI, Hex and Obviously AI. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Databricks AI | Freemium | Pricing not published | — |
| Hex | Freemium | Pricing not published | — |
| Obviously AI | Freemium | Pricing not published | — |
Alternatives to Dataiku
View allDatabricks AI
Databricks runs your lakehouse data, analytics and production AI agents on one governed platform across AWS, Azure and GCP.
Hex
Hex is an AI analytics workspace where a conversational agent and a SQL/Python notebook share one governed context layer.
Obviously AI
Obviously AI is now Zams — the archived obviously.ai domain shows the old no-code machine learning workflow: CSV in, predictive model out,
Tools that pair well with Dataiku
Common stack mates teams adopt alongside Dataiku, with the specific reason each pairing earns its keep.
Databricks AI
Databricks runs your lakehouse data, analytics and production AI agents on one governed platform across AWS, Azure and GCP.
Hex
Hex is an AI analytics workspace where a conversational agent and a SQL/Python notebook share one governed context layer.
Obviously AI
Obviously AI is now Zams — the archived obviously.ai domain shows the old no-code machine learning workflow: CSV in, predictive model out,
Frequently asked questions
Best-of guides
For makers of Dataiku
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