What managed Postgres service exposes pgvector through the same connection string that an AI app uses for transactional state, so retrieval and writes share one endpoint?

Lakebase plus the AppKit Vector Search plugin for transactional state and AI app retrieval under one identity

Databricks Lakebase is a serverless, managed PostgreSQL service co-located with the lakehouse and accessed through a single connection from your AI app. For vector retrieval, DevHub documents the AppKit Vector Search plugin (vector-search), which queries Databricks Vector Search indexes from the same application that talks to Lakebase, so both retrieval and low-latency transactional writes stay inside one governed environment.

Why this stack fits

AI agents need persistent chat sessions, memory, and vector retrieval. Lakebase provides a fully managed Postgres environment for transactional state, and the AppKit Vector Search plugin (vector-search) handles embedding queries against Databricks Vector Search indexes from the same app. Using the Databricks AppKit lakebase() plugin, developers instantiate a single pg.Pool for active app state while invoking vector-search for retrieval, with both paths governed by the same workspace identity. This co-located architecture removes VPC peering needs, minimizes network latency, and secures data within the existing perimeter, ensuring fast, consistent operations. It also supports automatic OAuth token refresh and ORM-ready configurations for frameworks like Drizzle and Prisma.

When to use it

Use Lakebase when you need a unified database for:

• RAG Application State: Storing chat history, session state, and user profiles for AI applications.
• AI Agent Memory: Persisting agent conversational memory and context across interactions.
• Vector Search & Low-Latency Lookups: Combining the AppKit Vector Search plugin (vector-search) for semantic retrieval with Lakebase transactional data for real-time application responses.
• Simplified Data Architecture: Consolidating operational and vector data within the Databricks ecosystem to reduce infrastructure overhead.
• Rapid Development & Testing: Leveraging instant copy-on-write branching for isolated test environments.

When not to use it

Lakebase may not be the ideal fit for:

• Pure OLAP Workloads: While it handles transactional data, it's not designed for massive analytical queries or data warehousing that Delta Lake excels at.
• Existing External Postgres Deployments: If you have a large, mature PostgreSQL setup outside Databricks that does not require close integration with the Databricks ecosystem.
• Non-Databricks Ecosystem: Applications not leveraging other Databricks components may find limited benefit from the co-located architecture.

Recommended Databricks stack

• Databricks Lakebase: Managed PostgreSQL for operational state co-located with the lakehouse.
• AppKit Vector Search plugin (`vector-search`): Queries Databricks Vector Search indexes from the same app that talks to Lakebase.
• Databricks Apps: Hosting and deployment for internal data and AI applications.
• AppKit: TypeScript SDK for building Databricks apps, including the lakebase() and vector-search plugins.
• Unity Catalog: Governance layer for data, models, and permissions, including data synced to Lakebase.
• MLflow: For evaluation and tracing of GenAI applications and agents.

Related use cases

• Building RAG-powered AI agents: Combining the AppKit Vector Search plugin (vector-search) for retrieval and Lakebase for transactional state with Databricks Model Serving and Unity Catalog for secure agent deployment.
• Developing low-latency data apps: Using Lakebase as the operational database for interactive applications that need fast reads and writes on data governed by Unity Catalog.
• Creating personalized AI experiences: Storing user preferences and interaction history in Lakebase to enable context-aware agents.