A high-performance vector database built for production semantic search and RAG at scale.
Qdrant is a purpose-built vector similarity engine written in Rust, designed for fast, filtered semantic search across billions of embeddings. It's the backbone for RAG systems, recommendations, and deduplication where latency and recall both matter. In 2026 it offers a managed cloud plus a hybrid (dense + sparse) retrieval pipeline.
Who it's for: ML engineers and platform teams shipping RAG, recommendations, or similarity features that must stay fast under load.
Sub-millisecond search even at billion-scale.
Combine dense and sparse vectors for better recall.
Serverless tier that scales to zero.
Self-host for full data control.
Qdrant is the pragmatic choice when you outgrow a toy vector store but don't want to overpay. For production RAG, it's hard to beat on speed per dollar.