Databricks

The data-and-AI lakehouse: Spark, Unity Catalog, Mosaic AI and model serving in one governed platform — Snowflake's rival when the work is pipelines plus models, not SQL plus Cortex.

Product preview

Overview

Databricks is the platform that refuses to split data engineering from model serving: the same catalog, the same notebooks, Mosaic AI on top. Snowflake Cortex is SQL-shaped AI; Databricks is pipeline-shaped AI. Hex sits on either warehouse; this is the warehouse that wants to be the ML platform too.

It is expensive, Spark-flavored and a career. For teams already on Delta it is the default AI add; for a Snowflake shop it is a migration sermon. Governance via Unity Catalog is the reason enterprises stay.

Pros / Cons

Pros

Pipelines and models under one catalog
Unity Catalog for enterprise governance
Mosaic AI without a second ML platform
Default when Delta is already the lake

Cons

Spark-shaped learning curve
Cost is a line item, then a career
Wrong default if the warehouse is already Snowflake

Who it's for

Training and serving models next to Delta tables

Governed vector search in the lakehouse

Notebook jobs that become production pipelines

Replacing a bolted-on ML platform

Bottom line

The lakehouse that wants to be the ML platform — pick it when Delta is already home.

Key Features

Lakehouse of Spark plus Delta

Unity Catalog governance

Mosaic AI model training and serving

Vector search in the same platform

Notebooks that become jobs

Compare Databricks with alternatives

Databricks vs Hugging Face Databricks vs Replicate Databricks vs Ollama 4 products · AI Platforms

Frequently asked questions

What is Databricks?

Databricks is a AI Platforms tool developed by Databricks. The company was founded in 2013 and is headquartered in San Francisco, USA. The data-and-AI lakehouse: Spark, Unity Catalog, Mosaic AI and model serving in one governed platform — Snowflake's rival when the work is pipelines plus models, not SQL plus Cortex.