Vector databases have become popular for AI workloads, but most enterprise applications need more than just vector search. They need transactions, analytics, metadata, and real-time performance in a single system.

In this session, Aasawari will explain why unified databases are better suited for enterprise AI workloads. She’ll walk through common AI patterns, where standalone vector databases fall short, and how a unified database approach simplifies architecture while improving performance and reliability.

What You’ll Learn:

•  Where vector databases fit and where they struggle in enterprise AI systems

•  Why combining vectors, transactions, and analytics matters

•  How unified databases simplify AI architecture and reduce operational overhead

•  Real-world examples of enterprise AI workloads built on a single platform

Speakers