QDRANT-SPEC

Qdrant Rust vector DB performance focus Apache 2.0.

Definition

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, AlmostHNSW recent improvements integration. (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 or 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.

Origin

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

Example in context

Twitch Search uses 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 to float32 baseline.

Related terms

• Weaviate — OSS Go competitor.