Qdrant

Qdrant allows attaching JSON payloads to vectors and supports complex filtering with keyword matching, full-text search, numerical ranges, and geo-locations, making it versatile for faceted search and business logic.

It combines dense and sparse vectors to address limitations of pure embeddings, enabling keyword-aware semantic search that improves accuracy for specific queries.

Built-in vector quantization reduces RAM usage by up to 97%, dynamically balancing search speed and precision, which is critical for cost-effective large-scale deployments.

Supports horizontal scaling via sharding and replication with zero-downtime updates, ensuring high availability and performance under load, as highlighted in the distributed deployment features.