Dataiku alternatives

3 tools to consider instead of Dataiku, shown against it.

Dataiku Alteryx DataRobot Domino Data Lab
Vendor Dataiku Alteryx, Inc. DataRobot, Inc. Domino Data Lab, Inc.
Pricing model Quote only Subscription Quote only Quote only
Free tier Yes No No No
Deployment Cloud, Self-hosted Cloud, Self-hosted Cloud, Self-hosted Cloud, Self-hosted
Open source No No No No
Best for Mixed teams of analysts and data scientists collaborating on the same data-to-model workflow. Business analysts automating data blending and prep work who also need basic predictive modeling. Enterprises wanting to automate model building and governance across many use cases at scale. Regulated enterprises needing centralized governance over many data scientists' compute and models.
Pricing

A free edition is available for individuals/small teams; paid editions require a custom quote from sales.

Pricing has not been verified yet — see the vendor's site.

Starter edition is a published per-user monthly price; Professional and Enterprise require a custom quote.

Starter $250/user/month
Professional Contact sales
Enterprise Contact sales

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

No published pricing; DataRobot requires a demo request and custom quote from sales.

Checked on the vendor's own page on September 21, 2026: no prices are published. Expect to be quoted.

No published pricing; Domino sells subscriptions by quote across Domino Cloud, Premium, and Enterprise self-managed tiers.

Checked on the vendor's own page on September 21, 2026: no prices are published. Expect to be quoted.

Features
  • Visual data preparation flow
  • Python and R notebooks alongside no-code recipes
  • AutoML and visual ML model building
  • MLOps: deployment, monitoring, and model versioning
  • LLM Mesh for generative AI application building
  • Collaborative project workspace for mixed-skill teams
  • Governance and data lineage tracking
  • Drag-and-drop workflow designer
  • Data blending across files, databases, and cloud sources
  • Built-in predictive and statistical modeling tools
  • R and Python code integration
  • Workflow automation and scheduling
  • Spatial/location analytics
  • Role-based access (viewer, basic creator, full creator)
  • Automated machine learning (AutoML)
  • Generative AI application building and evaluation
  • Model deployment and monitoring (MLOps)
  • Governance and approval workflows
  • Model explainability and bias testing
  • Time series and demand forecasting
  • Self-managed and cloud deployment options
  • Managed compute for notebooks, IDEs, and jobs
  • Bring-your-own-framework model development
  • Centralized reproducibility and experiment tracking
  • Model deployment and monitoring
  • GPU and distributed compute support
  • Self-managed VPC/on-premises deployment option
  • Governance and cost controls for IT/admins

In the index now