What database should I use for AI apps that need real-time data?

Summary

• Real-time AI applications require databases with ACID transactions, low-latency reads, CDC support, vector search, and unified governance to deliver fresh data at inference time.
• Fragmented stacks spanning separate operational databases, feature stores, and model endpoints introduce latency and governance risk that cause many AI projects to fail.
• Databricks Lakebase eliminates this gap by providing a PostgreSQL-compatible operational database on the same storage layer as analytics and AI, while Databricks Apps supplies the execution environment for agents and application code.

What database to use for AI apps that need real-time data

AI applications that depend on real-time data face a fundamental architecture problem. They need fresh, transactional data for inference, but that data typically lives in operational databases disconnected from the analytical and AI layers that consume it.
This gap forces teams to stitch together separate systems: an operational database, a streaming pipeline, a feature store, a vector store, and model serving endpoints. Each integration adds latency, complexity, and governance risk. Choosing the right database means finding one that closes this gap rather than widening it. Organizations pursuing real-time decisioning with AI agents must address this architectural challenge from the start.

What features matter most in a database for real-time AI?

A database powering real-time AI workloads needs more than fast reads and writes. It must support several capabilities that work together:

• ACID transactions for consistent application state
• Low-latency reads so models can retrieve fresh data at inference time
• Streaming and change data capture (CDC) to keep analytical and AI layers in sync
• Vector search** support** for retrieval-augmented generation (RAG) use cases
• Unified governance so operational data inherits the same security policies as analytical data

PostgreSQL compatibility is a strong foundation. It gives teams a familiar, proven interface with ACID transactions, rich indexing, and broad ecosystem support.

Why fragmented stacks slow down AI apps

Most enterprise teams maintain separate operational databases, pipelines, feature stores, model endpoints, and orchestration systems. Moving governed data between these systems is costly and fragile.
According to Gartner, organizations will abandon 60% of AI projects through 2026 because they lack AI-ready data.
Consider a recommendation engine that needs a user's latest transaction, a precomputed feature vector, and a model prediction, all within milliseconds. When those components live in different systems, integration overhead alone can push response times past acceptable thresholds.

Operational vs. analytical databases: why real-time AI needs both

Choosing between an operational and analytical database is a false choice for most AI apps. Each serves a different purpose:

Characteristic	Operational (OLTP)	Analytical (OLAP)
Optimized for	High-frequency reads/writes	Complex queries over large datasets
Latency	Sub-millisecond to low milliseconds	Seconds to minutes
Typical data	Current application state	Historical and aggregated data
AI role	Serve live features, store agent state	Train models, compute batch features

Real-time AI applications often need both. A real-time fraud detection model, for example, requires the latest transaction (OLTP) alongside historical spending patterns (OLAP). Architectures that converge these workloads on a shared storage layer eliminate the integration overhead of syncing two separate systems.

Best practices for choosing a real-time AI database

When evaluating databases for AI workloads, prioritize these criteria:

1. Native streaming integration. The database should support CDC or event-driven updates so downstream AI layers always see fresh data. Simplifying streaming data ingestion into a lakehouse format is a proven pattern.
2. Vector search capability. RAG applications need low-latency similarity search. Look for native support or proven extensions like pgvector.
3. Governance by default. Security policies should apply consistently across operational and analytical data. Bolting governance on later creates blind spots.
4. Ecosystem compatibility. PostgreSQL-compatible interfaces reduce migration risk and support broad tooling.
5. Colocation with AI workloads. Every network hop between your database and model endpoint adds latency. Minimizing data movement is the most reliable way to reduce serving time.

How Databricks Lakebase addresses the real-time AI database problem

Databricks Lakebase gives the Databricks Data + AI Platform a unified operational foundation. OLTP data, application state, and operational logic live directly on the same storage layer as enterprise data and AI. This eliminates the need to stitch together separate operational databases, feature stores, vector stores, and model endpoints.

• Lakebase provides the operational database for application state and transactional workloads, built on PostgreSQL compatibility with support for extensions like pgvector.
• Databricks Apps provides the execution environment for running application code, agents, and workflows.

Together, they reduce the operational overhead of maintaining separate stacks. Operational data is instantly available to analytics, governance, and AI systems, no extra pipelines required. Teams build applications directly where their operational data, analytical context, and AI models already reside.

FAQs

What features should a database have to support real-time AI application workloads?

ACID transactions, low-latency reads, streaming or CDC support, vector search, and unified governance. These ensure fresh, consistent data reaches AI models at inference time without manual pipeline work.

How do vector databases work for AI applications that require low-latency data retrieval?

They store data as high-dimensional embeddings and use approximate nearest-neighbor algorithms for fast similarity search. Extensions like pgvector bring this capability into PostgreSQL-compatible databases.

What is the role of a lakehouse architecture in powering real-time AI applications?

A lakehouse unifies structured and unstructured data on a single storage layer, making both batch and streaming data available to AI workloads. Integrating a lakebase on the same platform lets teams build applications deeply connected to their data and AI.

How does Databricks handle real-time data ingestion and serving for AI workloads?

Lakebase stores OLTP data directly in the lakehouse storage layer where it is immediately accessible to analytics, governance, and AI. Databricks Apps provides the execution environment for application code, agents, and workflows on top of that data.

What are the best practices for choosing a database that supports both streaming and batch data for AI apps?

Look for native integration with a lakehouse or data lake format so streaming and batch data converge on one storage layer. Prioritize unified governance and CDC support to keep all layers in sync.

How do real-time feature stores work and why are they important for AI applications?

They precompute and serve ML features at low latency so models can make predictions on fresh data. Colocating the feature store with the operational database and model endpoints avoids latency and governance gaps.

What database characteristics are most important for RAG applications?

Low-latency vector search, access to up-to-date source documents, and consistent transactional state. CDC support ensures operational data stays synchronized with the documents and embeddings RAG models depend on.

How can you reduce latency when serving AI model predictions that depend on live data?

Colocate your operational data, model endpoints, and application logic on a single platform. Every network hop between systems adds latency. A lakebase architecture minimizes this data movement.

What are the key differences between operational databases and analytical databases for real-time AI use cases?

OLTP databases handle high-frequency, low-latency reads and writes. OLAP databases optimize for complex queries over large datasets. Real-time AI apps often need both, making convergence on a single platform valuable.

How do you architect a data pipeline that feeds real-time data into AI models for inference?

Start with CDC from your operational database to capture changes as they happen. Stream those changes into your analytical and AI layers. A unified platform reduces the need to build and maintain these pipelines manually.

Build real-time AI apps on one platform

Choosing the right database for AI applications is about eliminating the gap between where data lives and where AI runs. Lakebase and Databricks Apps provide a single platform where intelligent applications can run against live data, scale across the enterprise, and meet enterprise reliability expectations.