OLTP on the Object Store: Reimagining Postgres with the Lakebase Architecture

Traditional databases force architects into a familiar set of tradeoffs: scale compute and you pay for storage you don't need, add read replicas for analytical queries and you introduce replication lag, separate your OLTP and OLAP systems and you're managing data movement and consistency across boundaries. These aren't bugs in the design; they're consequences of tightly coupled storage and compute.

Lakebase decouples storage from compute and runs Postgres natively on object storage. In this session, you'll learn what that architecture looks like, why it changes the tradeoff calculus, and what it makes possible: analytical queries running directly on operational data without read replicas, compute that scales to zero when idle, and transactional workloads sitting alongside lakehouse analytics on the same underlying data.

We'll map the architecture to concrete use cases, including real-time applications and AI workloads that need fresh operational data, and hybrid workloads that previously required two separate systems. You'll leave with a clear mental model of how Lakebase works and where it fits in your stack.