# 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.

Author: Simon Hearne (https://simonhearne.com/about/)
Published: 2026-08-19
Canonical: https://simonhearne.com/2026/milvus_search_gap/
Tags: VectorDB, Zilliz, Milvus, video, talk
Video: https://www.youtube.com/watch?v=XxcA1NSQnCo
Slides: https://talks.simonhearne.com/2026-08-milvus-search-gap/
Link: Test it yourself!: https://github.com/simonhearne/milvus_es_lab

---
The follow-up webinar, [Migrating to Milvus](/2026/migrating-to-milvus-talk/), 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](https://zilliz.com/event/faster-more-powerful-full-text-search-milvus-3-0) on 19 August 2026.