Elasticsearch vs Weaviate

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.	Weaviate Open-source, cloud-native vector database in Go that stores both objects and vectors.
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	+ Hybrid vector plus BM25 keyword search in a single query API + Built-in vectorizer integrations generate embeddings at ingest time + Multi-tenancy designed for tens of thousands of tenants + Enterprise features including RBAC, SOC 2, and HIPAA compliance options
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	− HNSW indexes are memory-hungry, driving up infrastructure cost at large scale − Collection/schema and module configuration adds setup complexity versus simpler stores − Expanding product surface (agents, embeddings, memory) increases the learning surface beyond core vector search
Used in stacks	0	0