Lakehouse Eliminates External AI Data Movement

The strategic shift is that Databricks and Snowflake are turning the data warehouse itself into the place where AI gets built and run. That matters because a team can keep tables, files, permissions, lineage, and model outputs inside one governed system instead of copying data into a separate AI tool. For DataRobot, that narrows the wedge from core model building toward governance, deployment control, and regulated use cases where a dedicated AI layer still earns its keep.

• On Databricks, the same platform now covers lake storage, SQL analytics, ML tooling, and agent development. Unity Catalog applies one permission and lineage system across data and AI assets, and Agent Bricks is designed to work on governed data already inside the platform.
  2 sacra 3 databricks 4 databricks
• Snowflake is making the same move from the warehouse side. Cortex AI Functions run inside Snowflake SQL, support text, image, audio, and video workflows, and are positioned as eliminating external services and data movement for many AI pipelines.
  5 snowflake 6 snowflake
• The result is that independent AI platforms increasingly look like an extra layer rather than the system of record. That is why neighboring vendors such as Dataiku have leaned into a GUI and workflow layer on top of Databricks and Snowflake, while DataRobot has emphasized observability, governance, and flexible deployment.
  1 sacra 7 sacra

Going forward, the center of gravity in enterprise AI moves closer to the governed data plane. Databricks and Snowflake will keep absorbing more model building and agent workflows, while standalone platforms win where companies need cross cloud control, stricter compliance workflows, or a simpler operating layer across many models and environments.