Qdrant vs Milvus: Rust-Speed Vector Search Benchmarked (2026)

Pros

• Written in Rust — best single-node query throughput benchmarks
• Payload-based filtering with no performance degradation (filtered HNSW)
• Simple deployment: single binary, no etcd or MinIO dependencies
• On-disk indexing (Qdrant 1.x) — handles datasets larger than RAM
• GRPC + REST API, Rust/Python/Go/TypeScript clients

Cons

• Horizontal scaling requires Qdrant Cloud or careful distributed setup
• Smaller community than Milvus
• Fewer index algorithm options (HNSW-focused; no IVF_FLAT, DiskANN natively)
• Less mature GPU acceleration vs Milvus

Pros

• Widest index selection: HNSW, IVF_FLAT, IVF_SQ8, DiskANN, GPU-IVF_PQ
• GPU-accelerated indexing: 10–100x faster build times with NVIDIA Rapids
• Battle-tested at billion-vector scale in production (Alibaba, ByteDance)
• Streaming ingestion with Kafka/Pulsar integration
• Partitions and dynamic schema for multi-tenant workloads

Cons

• Heavy deployment: etcd (consensus), MinIO/S3 (storage), multiple microservices
• Higher operational burden — tuning IVF parameters requires expertise
• Higher latency than Qdrant on filtered queries at similar hardware
• Milvus Lite not suitable for production QPS