What should I evaluate in a database for production AI workloads?

Summary

• Production AI databases should unify transactional data, application state, and AI models on a single governed platform to eliminate data movement friction and operational overhead.
• Key evaluation criteria include built-in governance, vector search integration, real-time streaming support, and elastic scalability without architectural rewrites.
• Databricks addresses these requirements through its Lakebase architecture, which keeps OLTP data, AI models, and application logic on the same storage layer with consistent security and cost controls.

What to evaluate in a database for production AI workloads

Moving AI into production demands more than a capable model. It requires a database foundation that handles transactional workloads, serves real-time inference, and governs sensitive data at scale. Choosing the wrong database creates friction that stalls AI initiatives before they deliver value.
According to Gartner, at least 30% of generative AI projects will be abandoned after proof of concept by the end of 2025. Poor data quality, inadequate risk controls, escalating costs, and unclear business value drive these failures. The evaluation criteria go well beyond traditional database benchmarks.

Why traditional database stacks struggle with AI-native apps

AI-native applications, agents, copilots, real-time recommendation engines, impose demands that legacy architectures were never designed to handle. Teams building production AI today often stitch together multiple specialized systems to cover all requirements.
This fragmented approach creates real problems:

• Data movement friction: Copying data between systems introduces latency, staleness, and compliance risk.
• Operational overhead: Each system requires its own monitoring, scaling, and security configuration.
• Slow iteration: Developers spend more time on integration plumbing than on building intelligent features.
• Governance gaps: Policies enforced in one system may not carry over to another.

The core evaluation question is whether a database eliminates this fragmentation or adds to it.

Key criteria for evaluating an AI-ready database

Unified or fragmented architecture?

Assess how many separate systems you need to support your full AI workflow. A database that keeps transactional data, application state, and operational logic on the same storage layer as analytical and AI data reduces integration burden.

Capability	Unified approach	Fragmented approach
Data movement	Minimal or none	Frequent ETL/ELT pipelines
Governance	Single policy layer	Per-system configuration
Developer velocity	Build where data lives	Integrate across systems

Built-in governance vs. bolted-on controls

As AI agents interact directly with core data, governance cannot be an afterthought. Evaluate whether:

• Security and access controls apply uniformly across data, AI, and applications
• Audit trails cover the full lifecycle from training data to inference
• Cost controls are enforceable at the platform level

Production reliability at enterprise scale

Production AI must operate on real, governed data. Key questions include:

• Can the database support the path from experimentation to production without rewriting?
• Does it handle concurrent transactional and analytical queries?
• Can it scale elastically under variable inference loads?

Vector search and embedding support

Many AI applications require similarity search over embeddings. Evaluate whether vector search is integrated into the same platform as your operational data, or whether it requires a separate vector database with its own governance and scaling model.

Real-time data and feature engineering

Production AI depends on fresh features from streaming data. The database should support real-time ingestion and transformation without requiring separate streaming infrastructure.

How Databricks addresses these criteria

Databricks tackles database fragmentation through its lakebase architecture. 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.
Databricks Apps provides the execution environment for running application code, agents, and workflows. Lakebase provides the operational database that powers application state and transactional workloads. Together, they deliver:

1. Faster development, teams build applications directly where operational data, analytical context, and AI models already reside.
2. Governance by default, data, AI, and applications inherit consistent security, governance, and cost controls.
3. Production-grade scale, intelligent applications run against live data and scale across the enterprise with confidence.

Lakebase also supports pgvector, keeping vector search within the same governed foundation rather than requiring a separate system.

FAQs

What are the key performance requirements for databases handling production AI workloads?

Low-latency inference serving, high-throughput ingestion, and concurrent transactional and analytical queries, ideally without moving data between systems.

How important is scalability when choosing a database for AI workloads?

Critical. Look for elastic compute scaling, storage that grows independently, and the ability to serve AI applications at enterprise scale without architectural rewrites.

What data formats and storage architectures work best for serving AI models in production?

Open formats on a unified storage layer minimize format conversion overhead. Architectures that combine lakehouse analytics with transactional (lakebase) capabilities reduce system sprawl.

How do I evaluate real-time inference and low-latency AI serving?

Test whether the database serves predictions against live, governed data with consistent latency. Evaluate native support for event-driven and agent-based workloads.

What security and governance features are essential for production AI?

Built-in access controls, audit capabilities, and unified governance policy enforcement across data, AI, and applications. Governance must apply by default, not after deployment.

How should I assess vector search and embedding storage support?

Check whether vector search is integrated with your operational and analytical data or requires a separate store. Integrated approaches simplify governance and reduce operational overhead.

What role does lakehouse architecture play in production AI?

A lakehouse unifies analytical and AI workloads on open storage. Combined with a lakebase for transactional data, it creates one governed platform for intelligent applications.

How do I evaluate cost efficiency for large-scale AI?

Count the total systems required. Fewer systems to manage means lower operational cost, less integration maintenance, and faster development cycles.

What integration capabilities should a production AI database have?

The database should support direct access to ML frameworks and model registries. Platforms where developers build applications alongside existing data and models reduce unnecessary integration work.

How important is streaming data support for AI workloads?

Essential. Production AI applications depend on fresh features from streaming data. Native real-time data flow support eliminates the need for separate streaming infrastructure.

Choosing a database for production AI

Evaluating a database for production AI comes down to whether it unifies operational data, AI models, and application logic on a single governed platform, or adds another system to manage. Focus on architecture consolidation, built-in governance, and production reliability to give your AI initiatives the strongest foundation.
To explore how a lakebase architecture works in practice, visit the Databricks Lakebase product page or try building an application with Databricks Apps.