Elasticsearch vs Milvus

Pick two tools from the same category to compare side-by-side.

	Elasticsearch Distributed search engine whose dense_vector kNN and hybrid retrieval support vector and RAG workloads.	Milvus Open-source, cloud-native vector database for large-scale ANN search, with Zilliz Cloud as its managed offering.
Category	retrieval	retrieval
Vendor type	hybrid	hybrid
Pricing	freemium	freemium
Strengths	+ Mature, scalable engine with strong operational and ecosystem tooling + Native hybrid search combining BM25 lexical and kNN vector retrieval + Inference API integrates embedding and reranking models for RAG + Available self-managed or as a managed Elastic Cloud service	+ Scales to billions of vectors via a disaggregated, distributed architecture + Three deployment modes span prototyping (Lite) to enterprise clusters + Broad index-type support and hybrid/multi-vector search + Neutral governance under LF AI & Data with a large contributor base
Tradeoffs	− Heavier to operate than purpose-built embedded vector stores − Vector features are newer than its long-established lexical search − Cluster tuning and resource sizing add operational overhead	− Distributed mode depends on multiple external components (etcd, message queue, object storage), making self-hosted operations complex − Heavyweight for small or single-node workloads where simpler stores suffice − Feature differences between Lite, Standalone, and Distributed modes can complicate migration paths
Used in stacks	0	0