Milvus vs Qdrant

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.	Qdrant Rust-based vector similarity search engine and database with payload filtering.
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	+ Single-stage metadata filtering during HNSW traversal rather than pre/post-filtering + Quantization options (scalar, product, binary) cut memory footprint substantially + Native hybrid search with dense, sparse, and late-interaction reranking + Rust implementation with REST/gRPC APIs and official Python/JS clients
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	− No built-in embedding modules historically; you manage vectorization yourself (cloud inference is newer) − Distributed mode requires manual sharding and replication decisions compared to fully elastic systems − Less natural fit than pgvector for teams wanting vectors inside an existing Postgres stack
Used in stacks	0	0

Milvus vs Qdrant

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