Vector Lakebase: Rethinking Data Infrastructure for AI-Native Applications
Description As AI applications move from prototypes to production, traditional data infrastructure is starting to show its limits. Systems designed for analytics or transactions struggle to support modern AI workloads — especially vector search, hybrid filtering, and multi-modal data at scale.
In this talk, we present Milvus and the concept of a Vector Lakebase — an AI-native data platform that unifies vector search with lakehouse storage. Instead of building yet another standalone vector database, Vector Lakebase brings indexing and retrieval directly to data lakes such as Iceberg and Paimon.
We’ll dive into the architecture and key design decisions behind Milvus, including:
How to enable efficient vector search directly on Iceberg/Paimon tables without data duplication Zero-copy data access: separating compute and storage while avoiding costly data movement Snapshot-aligned indexing: keeping vector indexes consistent with lakehouse metadata Handling production challenges like multi-tenancy, filtering, and cold-start performance
We’ll also share real-world patterns where this architecture matters most:
Agentic search systems that require consistent, high-recall retrieval Data discovery across large, heterogeneous datasets Data deduplication and similarity-based data management at scale
Finally, we’ll discuss how AI-native data infrastructure is evolving — and why integrating vector search into the lake, rather than building isolated systems, is becoming the dominant pattern.
Speakers:
Li Liu: Director of Engineering
Li Liu is Director of Engineering at Zilliz and Technical Lead for Milvus. With many years of experience in databases and big-data processing, he now leads the development and maintenance of Zilliz’s core database systems. Previously, he was a Senior Engineer at Meta, where he designed and built the company’s streaming data framework for advertising. Li holds a Master of Information Technology degree from Carnegie Mellon University.
