Qdrant

Qdrant is a vector database designed specifically for AI systems requiring semantic search capabilities. Unlike traditional keyword-indexed databases, it stores and queries data based on vector embeddings—numerical representations of meaning—enabling applications to find semantically similar content at scale with microsecond latency.

Highlights

• Dense and sparse vector search allows both semantic and keyword-based matching in a single query
• Multiple vectors per object enable richer semantic representations and improved ranking
• Advanced metadata filtering with JSON-based conditions applied during search for efficiency
• Reranking and Maximum Marginal Relevance (MMR) produce diverse and contextually relevant results
• Quantization technology compresses storage needs by up to 64x with minimal quality loss
• Flexible deployment: managed cloud, open-source self-hosted, hybrid cloud, and edge computing

Built for teams implementing semantic search, RAG pipelines, and AI agents, Qdrant operates as both a fully managed cloud service with usage-based pricing and as a free open-source project for self-hosted deployment.