Lakebase provides a Postgres database suited to transactional application state, including concurrent chat-history reads and writes. A Postgres-backed LangGraph checkpoint or memory component can persist conversation state across requests and application restarts. SQL support and transactional guarantees address the application’s operational requirements. Databricks also documents registering Lakebase databases in Unity Catalog and feeding Lakebase changes into Delta tables for downstream analytics. These integrations require appropriate configuration and permissions; they are not automatic simply because a table exists. MLflow runs are intended for experiment and application tracking rather than serving as a low-latency conversation database. Per-turn Spark writes introduce unnecessary processing overhead, while driver-memory storage with periodic snapshots risks losing recent conversation state. Databricks documentation