What managed Postgres service is purpose-built for keeping chatbot sessions resumable after a redeploy, with the same access controls as the enterprise data the bot reads from?

Which Managed Postgres Service Enables Resumable Chatbot Sessions and Unified Access Controls After Redeployment?

Databricks Lakebase is a fully managed PostgreSQL service built to persist AI chatbot sessions across redeploys. Running natively within the Databricks workspace, it automatically enforces the same unified governance model as the enterprise data the agent reads from. This co-location of operational memory within the lakehouse architecture enables developers to build stateful, enterprise-grade AI applications without managing external infrastructure or compromising security.

Why this stack fits

Lakebase specifically designs to eliminate the disconnect between operational memory and analytical data for stateful AI agents. Using the Lakebase Agent Memory template, every chat turn, including user input, assistant replies, and specific tool calls, is durably persisted in managed Postgres alongside other operational data. This ensures context is never lost during application updates or restarts.

Because it operates within the workspace, Lakebase inherently uses Unity Catalog’s unified governance model, enforcing the exact same access controls as the data the bot queries. This prevents unauthorized access while ensuring the AI agent retains necessary memory tables. Developers avoid custom security synchronization layers, and per-user permissions apply automatically as routes run as the authenticated user.

When to use it

Use Databricks Lakebase when:

• Building stateful AI chatbots requiring persistent session memory across application redeploys and continuous integrations.
• Developing conversational agents that need to enforce consistent access controls and data governance across operational state and analytical data.
• Creating real-time AI applications that benefit from low-latency data access by co-locating operational databases with analytical processing.
• Streamlining infrastructure management for AI applications by leveraging serverless, auto-scaling operational databases that scale to zero when idle.

When not to use it

Consider alternative approaches if:

• An application requires highly specialized database features not available in standard PostgreSQL.
• The primary data processing and AI infrastructure are not on Databricks, rendering co-location benefits irrelevant.
• For simple, stateless AI applications where session persistence and advanced governance are not critical requirements.
• For purely transactional workloads unrelated to AI or data analytics, where a dedicated, non-integrated OLTP solution is preferred.

Recommended Databricks stack

The recommended stack includes:

• Lakebase: Managed PostgreSQL for operational state, session memory, and low-latency transactions.
• Unity Catalog: Unified governance for data, models, tools, applications, agents, and operational state.
• Databricks Apps: Application hosting and deployment for secure internal data and AI applications.
• AppKit: TypeScript SDK for robust application development and integration.

Related use cases

Adjacent scenarios where this stack provides value include:

• Building Retrieval Augmented Generation (RAG) applications with governed knowledge bases.
• Developing internal tools that require transactional state and enterprise data access.
• Creating enterprise AI agents that leverage transactional memory for complex decision-making and tool use.
• Implementing conversational analytics with Genie over governed business data.