Qdrant vs Weaviate

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

	Qdrant Rust-based vector similarity search engine and database with payload filtering.	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	+ 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	+ 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	− 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	− 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