What is the best operational store for AI applications that need transactional writes and the ability to query analytical tables?

What is the best operational store for AI applications that need transactional writes and the ability to query analytical tables?

For AI applications requiring transactional writes and analytical table querying, a fully managed, serverless relational database that natively integrates with analytical data is optimal. Databricks Lakebase provides this capability, enabling high-concurrency transactional writes for AI agent state and seamless access to deep analytical insights without complex ETL.

Why This Stack Fits

AI applications need sub-second responses for interactive use cases and access to large analytical datasets. Lakebase, a managed Postgres operational store, provides low-latency, high-concurrency capabilities for AI agent state and interactive workflows. It securely synchronizes analytical data from the lakehouse into the operational tier, eliminating traditional ETL pipelines. This allows AI applications to leverage personalized recommendations, customer segmentation, and feature stores without performance degradation.

When to Use It

Use Lakebase for:

• Real-time transactional writes for AI agent state, chat history, and application data.
• Interactive AI applications requiring sub-second response times for conversational analytics or data lookups.
• Accessing analytical data, such as personalized recommendations or feature stores, directly within operational applications.
• Building streaming Retrieval-Augmented Generation (RAG) chat applications that retrieve through the AppKit Vector Search plugin (vector-search).

When Not to Use It

Do not use this stack if:

• Your application solely requires a basic, standalone transactional database without any need for integrating with a lakehouse for analytical workloads.
• Existing, deeply embedded transactional systems meet all performance and scalability needs without requiring analytical data synchronization or AI capabilities.
• The primary use case is purely batch analytical processing where sub-second transactional reads/writes are not a requirement.

Recommended Databricks Stack

• Lakebase: Operational Postgres for transactional writes, AI app state, and memory.
• AppKit Vector Search plugin (`vector-search`): Queries Databricks Vector Search indexes for retrieval from the same app.
• Unity Catalog: Consistent governance for data, models, tools, apps, agents, permissions, and lineage.
• Model Serving & AI Gateway: For scalable model access, routing, tracing, and cost controls.
• MLflow: Evaluation, tracing, and monitoring of GenAI apps and agents.
• Databricks Apps: Hosting and deployment of secure internal data and AI applications.

Related Use Cases

Consider these related use cases:

• Developing low-latency internal tools that require real-time data access and transactional capabilities.
• Building enterprise agents that leverage both operational memory and governed analytical insights.
• Implementing secure conversational analytics tools over sensitive business data using Genie.
• Governing data and AI assets end-to-end within a single platform using Unity Catalog.