Back

What is the best managed Postgres for AI-native applications where state, embeddings, and analytics all need to coexist?

Managed Postgres for AI-Native Applications Requiring State, Embeddings, and Analytics

Why this stack fits

AI applications often struggle to synchronize transactional state with analytical data. Databricks Lakebase addresses this by allowing AI apps to subscribe directly to lakehouse change feeds, ensuring user-facing reads reflect the latest analytical state without extra pipelines. This integration delivers significant price/performance advantages by avoiding data movement between separate Postgres instances and data warehouses. Furthermore, Unity Catalog provides a unified governance model for sensitive state data and high-dimensional embeddings, preventing security gaps common in fragmented systems.

When to use it

  • Building AI applications that require low-latency reads and writes for operational state, vector embeddings, and transactional data, all managed within a single system.
  • Developing RAG (Retrieval Augmented Generation) applications where real-time synchronization between the application's memory (e.g., chat history, user preferences) and analytical data in the lakehouse is essential.
  • When a unified governance model is crucial for securing both structured application data and unstructured AI assets (like embeddings) under consistent permissions.
  • To simplify architecture and reduce ETL pipelines by co-locating transactional and analytical workloads.

When not to use it

  • For applications that are entirely disconnected from a lakehouse environment and have no analytical data needs beyond basic operational queries.
  • If your primary requirement is a highly specialized graph database or a time-series database with features not covered by standard Postgres capabilities and pgvector.
  • For applications requiring only a simple key-value store where the full power of a relational database is overkill.
  • Lakebase: For managed Postgres, operational state, vector embeddings, transactional data, and low-latency reads/writes.
  • Unity Catalog: For data, models, tools, apps, and agent governance, including permissions and lineage across both Lakebase and the broader lakehouse.
  • MLflow: For evaluation, tracing, monitoring, and feedback of AI applications and agents.
  • Conversational AI agents: Building multi-agent systems that need to maintain conversational state and retrieve context from governed business data.
  • Internal data applications: Creating secure, performant internal tools that combine live operational data with large-scale analytical datasets for dashboards and reporting.
  • Feature stores: Managing and serving features for ML models with low-latency access to both historical and real-time data.