Dataiku vs Databricks
Dataiku and Databricks are both evaluated in the CIOPages AI/ML Platforms Buyer's Guide. The guide groups Dataiku under Governed Self-Service and Databricks under Lakehouse + AI.
| Attribute | Dataiku | Databricks (Mosaic AI) |
|---|---|---|
| Approach | Governed Self-Service | Lakehouse + AI |
| Best for | Enterprises that want one governed, cloud-agnostic platform spanning data scientists and business analysts, with multi-LLM access under guardrails | Data-intensive enterprises that want training, serving, and governance to live on the same governed lakehouse as their data |
From the AI/ML Platforms Buyer's Guide, updated June 2026. 8 vendors evaluated.
Dataiku
One governed canvas for data scientists, analysts, and business users — and adopting its way of working is the price of admission: visual flows alongside code, AutoML, a feature store, and full MLOps for deployment and monitoring, with LLM Mesh adding a governed gateway that routes, secures, and audits access to multiple LLM providers, running cloud-agnostically across AWS, Azure, GCP, and on-prem. A consistent Gartner Magic Quadrant Leader, and still independent. It is less suited to cutting-edge ML research or the lowest-level custom-model work than a notebook-first or hyperscaler-native stack, and as an independent vendor reportedly moving toward an IPO, the roadmap is worth tracking as the company evolves.
Databricks (Mosaic AI)
Models train and serve next to governed data rather than a copy of it, and that is the whole argument: one lakehouse unifying data engineering, analytics, and ML, with Mosaic AI — built on the 2023 MosaicML acquisition — adding GPU training, fine-tuning, model serving, and a vector index, Unity Catalog supplying lineage and governance, and open-source MLflow, which the company stewards, as the registry and tracking backbone, multi-cloud across AWS, Azure, and GCP. Positioning is premium and the DBU consumption model is hard to forecast across compute types and serving endpoints. The platform pulls you toward the Databricks-and-Spark way of working, and smaller, notebook-light teams will find it heavier than the problem warrants.
Also evaluated in this guide
AWS SageMaker, Azure Machine Learning, DataRobot, Google Vertex AI, Snowflake Cortex, Weights & Biases
Editors choose the vendors and write the evaluations. Paying CIOPages does not change how a vendor is covered. Editorial policy