Use cases

Region availability

Lakebase Autoscaling is the latest version of Lakebase, with autoscaling compute, scale-to-zero, branching, and instant restore. For supported regions, see Region availability. If you are a Lakebase Provisioned user, see Lakebase Provisioned.

Lakebase Autoscaling supports three primary patterns: serve lakehouse data in Postgres, run an application backend, and power AI agents and ML. Each pattern uses Postgres alongside Unity Catalog to give your application a low-latency database that stays in sync with the lakehouse.

Serve lakehouse data

Synced tables bring Unity Catalog data into your Lakebase database for low-latency transactional reads. Pick a source table, choose a sync mode, and the pipeline is fully managed. No sync scripts, no external orchestration, no jobs to monitor. Continuous mode keeps data within seconds of the source. Triggered mode balances freshness and cost with scheduled incremental updates. Your application always serves the latest analytics alongside its own operational data.

First steps	Learning path
Create a synced table Schedule ongoing syncs	Sync modes — Snapshot, Triggered, or Continuous Data type mapping — UC to Postgres type compatibility Capacity planning — connection limits, throughput rates Register in Unity Catalog — cross-source queries across operational and analytical data

Application backend

Your application connects to Lakebase the same way it connects to any Postgres database. Use the drivers and frameworks you already know. When your app gets a traffic spike, autoscaling adds compute without dropping connections. When traffic stops, scale-to-zero suspends the database and reactivates in hundreds of milliseconds on the next query. You don't provision for peak and you don't pay for idle. For development, branching gives every developer an isolated copy of the production database with no data seeding, no storage duplication, and no waiting.

First steps	Learning path
Build a Databricks App — deploy a template with managed credentials Connect your own application — custom Databricks App, SDK, or API	How autoscaling works — compute behavior under load Scale to zero — suspension, reactivation, idle timeout How branches work — copy-on-write isolation for dev/test Authentication — token rotation for production apps

AI agents and ML

Lakebase serves as the backend for AI agent memory and real-time feature serving. Agents built with LangGraph or the OpenAI Agents SDK store conversation state and long-term memory in Postgres. Models served with Mosaic AI access feature data through Online Feature Stores that are powered by Lakebase Autoscaling. Both benefit from automatic scaling, scale-to-zero, and Unity Catalog governance.

First steps	Learning path
Deploy an agent on Databricks Apps — clone a template and deploy Agent state and memory — add short-term and long-term memory with Lakebase	Stateful agents on Apps — LangGraph checkpointers, memory templates Stateful agents on Model Serving — time travel, checkpoint management Feature Store and Model Serving — low-latency feature data powered by Lakebase Online Feature Stores — create, publish, and manage feature stores