QDRANT-SPEC

Qdrant Rust vector DB performance focus Apache 2.0.

Définition

Qdrant features : (1) Collections : analog to tables, defined with vector size + distance metric (Cosine, Dot, Euclidean, Manhattan). (2) Index : HNSW (Hierarchical Navigable Small World) optimized Rust implementation, integration AlmostHNSW recent improvements. (3) Quantization : Scalar Quantization (32-bit float -> 8-bit int4 4x compression), Binary Quantization (1-bit per dimension 32x compression, faster but accuracy loss), Product Quantization PQ. (4) Filtering : Payload-based filtering integrated index (Filterable Index), pre-filtering during HNSW traversal (vs post-filtering common alternatives). (5) Multi-tenancy : tenant isolation via collection separation ou payload-based. (6) Distributed : multi-node cluster support sharding + replication, Raft consensus, Kubernetes Helm chart. (7) Sparse Vectors : SPLADE, native support hybrid dense + sparse. (8) Performance : Rust efficiency, ~10x memory reduction vs Python Milvus equivalent, sub-10ms latency typical. Pricing : OSS free + Qdrant Cloud Free Tier (1GB storage) + Standard tier $0.05/GB-month + Hybrid Cloud BYOC. Customers : Twitch Search, Vivino, Bayer, HubSpot. $28M Series A 2024.

Origine

Qdrant fondee 2021 a Berlin par Andrey Vasnetsov + Andre Zayarni ; Seed $7.5M 2022 ; Series A $28M janvier 2024 (Spark Capital lead) ; ~50000+ self-hosted instances ; ~3000 Qdrant Cloud customers 2024.

Exemple en contexte

Twitch Search utilise Qdrant ~100M+ video clips embedded via custom multi-modal model (vision + audio + text), semantic search clips ~50ms latency, Qdrant cluster ~12 Kubernetes nodes c5.4xlarge, Binary Quantization reduces memory 32x compared float32 baseline.

Termes liés

• Weaviate — concurrent OSS Go.