Lakebase: Serverless Postgres over Open Lake Storage

Abstract

Modern cloud applications and AI-driven workloads demand elastic compute, instant environment isolation, and open data interoperability. However, production databases remain largely based on monolithic architectures designed in the 1980s, tightly coupling storage and compute and creating operational fragility, cost inefficiencies, and vendor lock-in. This paper presents Lakebase, a new open database architecture that decouples transactional compute from storage while placing database data directly in low-cost cloud object stores in open format. Unlike second-generation cloud databases, such as Amazon Aurora, Microsoft Socrates and Google AlloyDB, which separate storage internally but retain proprietary formats and single-engine access, Lakebase exposes storage as an open, durable lake layer while running elastic, serverless Post-greSQL transactional engines independently on top. We describe the system design principles behind Lakebase, including open-format object storage persistence, compute elasticity down to zero, instant branching and cloning via copy-on-write semantics, and unified transactional-analytical access over shared lake storage, as deployed in the Databricks Lakebase service. We analyze how this architecture reduces operational complexity, enables Git-like database workflows, improves failure recovery semantics, and mitigates structural vendor lock-in. We further discuss production deployment characteristics, performance trade-offs, durability guarantees, and support for multicloud high availability. We evaluate Lakebase on standard OLTP and analytical benchmarks, alongside production telemetry on branching and elasticity. Lakebase represents a third generation of database architecture: transactional systems rebuilt natively for cloud object storage, elastic compute, and AI-era development workflows.