Milvus vs Weaviate

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

	Milvus Open-source, cloud-native vector database for large-scale ANN search, with Zilliz Cloud as its managed offering.	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	+ 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	+ 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	− 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	− 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