Qdrant
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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.
Payload filters cover geographic criteria, dates and nested objects, among others. Qdrant handles several vectors per point for multimodal RAG and offers Python, JavaScript, Go and Rust SDKs.
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.