Dataiku
Enterprise AI platform for governed analytics, machine learning and AI agents.
Available information varies by company and source.
Profile record updated:
Company facts
- Official name
- Dataiku SAS
- Entity type
- COMPANY
- Founded
- 2013
- Headquarters
- France
- Company size
- 1,001–5,000
- Market role
- B2B SaaS Provider
- Official website
- dataiku.com
What Dataiku does
Dataiku sells enterprise AI software that unifies development, orchestration, deployment and governance across analytics, machine learning, LLM applications and AI agents. Customers pay recurring subscription or licence fees to use the platform in Dataiku Cloud or their own cloud and internal IT environments. Consumption-based AI Credits, agent-monitoring charges and associated enablement services extend recurring account value.
Category differentiation
Dataiku is enterprise AI and analytics software, not a foundation-model provider or cloud hyperscaler. It governs and orchestrates customer AI workloads across existing cloud, data and model environments.
Strategic context
AI-supported assessment from the existing company research; distinguish interpretation from sourced facts.
Dataiku SAS is a French private enterprise software company that provides a unified platform for data preparation, analytics, machine learning, generative AI and AI-agent development. The platform enables business, data, engineering, IT and governance teams to build, deploy, monitor and govern AI workloads across existing cloud, data and model infrastructure. It is available as managed SaaS and self-managed software in customer environments. Dataiku generates recurring revenue through enterprise subscriptions and software licences, supplemented by metered AI consumption, annual per-instance agent-management access, per-agent monitoring, and professional services. Its customers are large enterprises deploying governed analytics and AI at organisational scale.
Company news briefing
Briefing updated:
Dataiku has launched Dataiku Agent Management to support enterprise AI agent workflows and highlighted critical industry governance challenges at the Ai4 2026 conference, noting that the vast majority of enterprise leaders observe unsanctioned generative AI usage. Additionally, the platform has expanded its ecosystem integration footprint through recent collaborations, such as its inclusion in BMC Software's Model Context Protocol updates for hybrid enterprise environments.
Business model & monetisation
Dataiku monetises through recurring enterprise subscriptions for Dataiku Cloud and enterprise licences for self-managed deployments. AI Services use a metered credit model, including monthly usage allocated to paid Designer seats and paid Dataiku AI Credits beyond included allowances. Agent Management combines annual per-instance access fees with per-agent monitoring charges. Training, consulting, technical enablement and related services provide additional service-fee revenue.
- Dataiku Cloud subscriptions
- Recurring managed SaaS subscription fees
- Self-managed enterprise software licences
- Recurring enterprise licence and support terms
- AI Services usage
- Metered Dataiku AI Credits beyond included usage
- Agent Management
- Annual per-instance access plus per-agent monitoring
- Training, enablement and consulting
- Professional service fees
Products & capabilities
No products with linked sources are available in this view.
Products & market categories
Recent recorded signals
Dates refer to the source publication. Older entries are historical context, not evidence of a new event.
Discover Dataiku Agent Management: every agent on the record
Recorded impact score: 2/5
Dataiku announced the launch of Dataiku Agent Management, a new product feature for managing AI agents, along with the announcement of the AI Success Frontrunner award winners.
12,000 Attend Ai4 2026 to Discuss AI's Invisibility
AI · Recorded impact score: 2/5
The article reports on the Ai4 2026 conference in Las Vegas, which saw record attendance of over 12,000, up from 8,000 the previous year. Dataiku's keynote highlighted that 96% of enterprise leaders believe employees are using unsanctioned generative AI tools, and 80% of CIOs see their jobs at risk without measurable AI ROI. Pat Gelsinger argued that AI economics must improve dramatically, while Geoffrey Hinton, Fei-Fei Li, and Andrew Ng jointly endorsed AI regulation. The conference emphasized the need for AI governance, budget ownership, and cross-functional staffing. Speakers from Cisco, Nvidia, Uber, PayPal, and other companies discussed the shift from assistants to autonomous agents and the importance of accountable AI deployment.
- Ai4 2026 attendance exceeded 12,000, up from 8,000 the previous year.
- Dataiku's survey found 96% of enterprise leaders believe employees use unsanctioned generative AI tools.
Governed RAG: Data, Context & Lineage for Enterprise AI
Data & RAG Governance · Recorded impact score: 3/5
The article describes risks introduced by Retrieval-Augmented Generation (RAG) when enterprise data is exposed to vector search pipelines and proposes a three-part Governed RAG architecture: (1) ingestion with cryptographic embedding lineage and metadata, (2) query-time contextual Attribute-Based Access Control (ABAC) embedded into vector search queries, and (3) outbound payload sanitization (PII/PHI masking, indirect injection removal, and context length minimization). It argues that enterprises must enforce retrieval-time access controls, maintain graph-based data lineage, and implement real-time index freshness/eviction to prevent privilege escalation, prompt-injection attacks, stale-context hallucinations, and to meet compliance requirements.
- Retrieval-Augmented Generation (RAG) pairs LLMs with vector databases and knowledge graphs to ground agents in proprietary corporate knowledge.
- Vector stores typically do not preserve fine-grained document-level ACLs or cryptographic data lineage by default, creating over-permissioned retrieval risks.
Explore company relationships
Questions about Dataiku
What is Dataiku?
Dataiku is an enterprise AI platform for data preparation, analytics, machine learning, generative AI, AI agents, orchestration and governance.
Who uses Dataiku?
Large enterprises use it through data, engineering, IT, business, AI governance, risk and compliance teams.
How does Dataiku make money?
It earns recurring SaaS subscriptions and software licence fees, plus metered AI Credits, per-agent monitoring and professional-services fees.
Sources & coverage
This profile uses public, official and technically observable information. Missing information does not prove that a product or relationship does not exist. The list below does not imply that every profile statement has been verified.
23 publicly documented primary sources and citations linked across the market graph.
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