Webinar
Milvus 2.6 Deep Dive: Faster Search, Lower Cost, Smarter Scaling
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About this webinar
Milvus 2.6 introduces major upgrades that directly impact what developers and production users care about most: lower cost, faster search, and smoother scaling. From reduced memory usage to smarter storage and dramatically improved indexing, this release makes high-performance vector search more efficient than ever.
Join James Luan, VP of Engineering at Zilliz, for a focused walkthrough of the features that matter in real-world workloads — plus an exclusive first look at Milvus 3.0 and Milvus Lake. In just one hour, you’ll see live demos, learn practical architecture tips for RAG and agentic systems, and get direct access to the Milvus engineering team. If you’re building AI search, RAG, multimodal retrieval, or large-scale vector pipelines, this is the Milvus update you should not miss.
What You’ll Get
- A crisp breakdown of the biggest Milvus 2.6 upgrades
- Early access previews of Milvus 3.0 & Milvus Lake
- Live demos of advanced vector + hybrid + multimodal search
- Architecture guidance for real AI workloads
- Direct Q&A with Milvus engineers
Milvus 2.6 Highlighted Features
- Enhanced Full-Text Search — Up to 4× faster hybrid search for richer semantic + keyword retrieval
- Tiered Storage — Cost-optimized hot/cold data management, reducing storage costs by up to 50%
- RaBitQ 1-Bit Quantization — Up to 72% lower memory usage with faster vector queries
- Semantic + Geospatial Search (R-Tree) — Combine where things are with what they mean for more relevant results
- CAGRA + Vamana Hybrid Mode — Build on GPU, query on CPU, dramatically cutting deployment cost
- Accelerated JSON Filtering — Up to 100× faster complex field filtering
- 100K Collections per Cluster — A major step toward massive-scale multi-tenancy
- Dynamic Schemas — Evolve schema fields on the fly with zero downtime
- Multi-Modal Data Lake Discovery — Unified search across vectors, text, and structured data in one system
- And More...
Meet the Speaker
Join the session for live Q&A with the speaker
James Luan
VP of Engineering at Zilliz
James Luan is the VP of Engineering at Zilliz. With a master's degree in computer engineering from Cornell University, he has extensive experience as a Database Engineer at Oracle, Hedvig, and Alibaba Cloud. James played a crucial role in developing HBase, Alibaba Cloud's open-source database, and Lindorm, a self-developed NoSQL database. He is also a respected member of the Technical Advisory Committee of LF AI & Data Foundation, contributing his expertise to shaping the future of AI and data technologies.