Databricks vs Browse AI

Only in Databricks

Databricks

• • Lakehouse storage and compute that unifies batch streaming BI and ML on open formats for cost and portability across clouds
• • Collaborative notebooks and repos that let data and ML teams build together with version control alerts and CI friendly patterns
• • SQL Warehouses that power dashboards and ad hoc analysis with elastic clusters and fine grained governance via catalogs
• • MLflow native integration for experiment tracking packaging registry and deployment that works across jobs and services
• • Vector search and RAG building blocks that bring enterprise content into assistants under governance and observability
• • Jobs and workflows that schedule pipelines with retries alerts and asset lineage visible in Unity Catalog for audits

Databricks

• → Build governed data products that serve BI dashboards and ML models without copying data across silos
• → Modernize ETL by shifting to Delta pipelines that handle streaming and batch with fewer moving parts and clearer lineage
• → Deploy RAG assistants that search governed documents with vector indexes and access controls for safe retrieval
• → Scale experimentation with MLflow so teams compare runs promote models and enable reproducible releases
• → Consolidate legacy warehouses and data science clusters to reduce cost and drift while improving security posture
• → Serve predictive features to apps using online stores that sync from batch and streaming pipelines under catalog control

Databricks

data engineers analytics leaders ML engineers platform teams and architects at companies that want a governed lakehouse for ETL BI and production AI with usage based pricing