Lakehouse Serving: Onehouse LakeBase vs Databricks Lakebase Postgres

For years, the lakehouse unified storage and analytics. It did not unify serving. The architecture typically looked like this: Lakehouse → analytics & ETL Operational database → low-latency applications Reverse ETL → copy curated subsets between them That split worked when humans drove queries. AI agents changed the load profile. They issue iterative point lookups, selective filters, repeated joins, and parallel queries inside tight reasoning loops. That workload stresses both scan-optimized engines and traditional OLTP systems in different ways. Two architectural responses have emerged from Onehouse and Databricks. Onehouse LakeBase: Database Primitives on Open Tables LakeBase is positioned as a low-latency serving layer built directly on open lakehouse tables, specifically: Apache Hudi Apache Iceberg Storage remains object-store based. LakeBase introduces: Record-level and secondary indexing Index joins that shift cost toward O(K) for selective workloads Transaction-aware distributed