Simon Hearne
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VectorDB Zilliz Milvus video talk

Milvus 3.0: Closing the Search Gap

Milvus 3.0 closes the gap between vector search and full-text search: one engine for both. A customer-backed comparison of Milvus 3.0 and Elasticsearch 9.14, with live side-by-side queries you can reproduce yourself.
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The follow-up webinar, Migrating to Milvus, moves from comparison to migration: tooling, deployment choices and lessons from real migrations.

About the webinar

Milvus has long been known for vector search and has supported full-text search since Milvus 2.5, allowing teams to combine lexical and semantic retrieval in one engine. With Milvus 3.0, full-text search takes another major leap:

  • Compressed BM25 indexes roughly 3× smaller than those in Milvus 2.6 at comparable recall, reducing memory and bandwidth pressure.
  • SINDI, a new sparse-retrieval algorithm that delivered roughly 5× to 10× the QPS of MaxScore across four learned-sparse benchmarks, and extends the optimised retrieval path to native BM25.
  • Sorting, aggregation and faceting inside the engine.

With stronger full-text retrieval added to Milvus's established vector-search foundation, teams can reconsider whether AI retrieval still requires a separate full-text system, along with the infrastructure, pipelines and result-fusion logic that come with it. This talk is a customer-backed comparison of Elasticsearch and Milvus, including live side-by-side queries against Milvus 3.0 and Elasticsearch 9.14. Query semantics, results and performance are all reproducible with the open-source lab linked above.

What's covered

  • Milvus 3.0 full-text search. BM25, sparse retrieval, index efficiency, server-side sorting, aggregation and faceted search, with a brief look at planned fuzzy matching, multi-phase reranking and JSON-field aggregations.
  • Elasticsearch vs. Milvus. Search capabilities, performance and operational tradeoffs across the two systems.
  • Live query demo. The same queries in Elasticsearch and Milvus, with results and latency visible on screen.
  • Hybrid search and ranking. How full-text and vector results can be combined through ranking and fusion methods such as RRF.
  • Production customer evidence. How real teams improve retrieval quality, support growing workloads and simplify search operations.
  • Live AMA. A 20-minute discussion of search patterns and architecture questions.

Presented as a Zilliz webinar on 19 August 2026.