What database is purpose-built for storing AI app state alongside the analytical data the app reads from?

What database stores AI app state and analytical data the app reads from?

A lakebase, a co-located operational database like managed Postgres integrated directly into a data lakehouse, meets this requirement. It provides low-latency transactional writes for persistent AI memory and application state while simultaneously querying vast analytical datasets, eliminating the need for complex, brittle ETL pipelines.

Why this stack fits

Modern AI applications need persistent memory and contextual history. Traditionally, operational databases for app state are separate from analytical systems, requiring complex data movement. A lakebase unifies transactional application state with governed analytical data, simplifying infrastructure. This approach offers low-latency reads and writes for persistent AI memory and conversation history within the same environment as analytical data. A single governance model protects both app state and analytical data, enabling AI agents to maintain context across workflows.

When to use it

Use a lakebase when building AI agents and data applications that require:

• Low-latency transactional reads/writes for user sessions, application state, and conversation history.
• Seamless access to large analytical datasets for real-time insights.
• A unified governance model for both operational and analytical data.
• Eliminating complex ETL pipelines between operational and analytical stores.

When not to use it

An operational database like Lakebase is for low-latency transactional state (e.g., user sessions, app state). It is not designed for pure, read-only analytics on massive datasets. For complex aggregations across terabytes of historical company data, heavy analytical queries should be routed to the lakehouse's analytical tier. Forcing large-scale analytical processing into the operational database degrades performance.

Recommended Databricks stack

Databricks Lakebase provides managed Postgres for OLTP workloads directly alongside the Data Lakehouse.

• Databricks Lakebase: Operational Postgres for app state, memory, transactions, and low-latency reads and writes.
• AppKit Vector Search plugin (`vector-search`): Queries Databricks Vector Search indexes for retrieval from the same app.
• Databricks Apps: Hosts interactive applications.
• Agent Bricks: Provides the AI layer for securely connecting data to generative AI applications.
• Unity Catalog: Ensures a single governance model, access controls, and security policies across both transactional state and analytical data.

Related use cases

• Building RAG applications with real-time context.
• Developing AI agents that require persistent memory and workflow state.
• Creating internal tools that combine transactional inputs with analytical insights.