Founded

2013

Starting Price

Free

About Dataiku

Dataiku is an enterprise AI and data science platform that enables organizations to operationalize artificial intelligence at scale. The platform covers the full data lifecycle from ingestion through deployment and monitoring, providing both code-friendly and no-code interfaces for data scientists, analysts, and business users to collaborate on AI projects.

Pros & Cons

Pros

Comprehensive end-to-end platform covering full data lifecycle eliminates tool fragmentation
Flexible for all skill levels with visual tools and code support (Python, R, SQL)
Excellent collaboration features bridging technical and non-technical teams
Strong integration with 25+ data sources and flexible deployment (on-prem, cloud, SaaS)
Enterprise-grade governance with audit logging and compliance features

Key Features

AutoML & Visual ML

Automated feature generation, model selection, and rapid prototyping with configurable pre-built tools

Unified Data Preparation

Connect, cleanse, and prepare data from 25+ sources with visual and code-based workflows

MLOps & Model Management

Centralized deployment, monitoring, and automated drift detection with model evaluation tracking

Collaborative Visual Flows

Transparent data pipelines with visual workflows giving all stakeholders visibility into the AI lifecycle

Agent Connect Hub

Centralized deployment and routing for AI agents with governance controls and continuous optimization

Multi-Engine Support

Supports Spark, TensorFlow, Python, H2O, and 32+ algorithms with flexible deployment options

Pricing

Free Edition

Free/perpetual

Up to 3 concurrent users
Basic data preparation
Community support
Limited to small-scale projects

Best For

Manufacturing Quality Optimization

Deploy AI across global manufacturing sites to enhance product consistency and reduce defects

Enterprise Analytics Democratization

Enable analysts and business users to build and deploy ML models without coding expertise

Generative AI Prototyping

Rapidly develop and test generative AI applications across departments with collaborative tools

Tags:data science automl mlops enterprise ai collaborative analytics

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Featured In

#6

7 Best Enterprise AI Orchestration Platforms for Governed AI Deployment (2026)

Best for enterprises that need cross-team collaboration between data science and business stakeholders, with the strongest visual governance pipeline and centralized agent management to prevent sprawl across departments.

#5

7 Best AI Data Analytics Platforms for Non-Technical Teams (2026)

Best for organizations that want technical and non-technical teams collaborating on the same data platform — the bridge between 'give me a dashboard' and 'build me a prediction model.'

Dataiku