{"name":"Qdrant","slug":"qdrant","category":"vectordb","type":"hybrid","website":"https://qdrant.tech","pricing":"freemium","pricing_tiers":["Free (self-hosted, Apache 2.0)","Free Tier (1GB cluster)","Standard Tier (usage-based)","Premium Tier (minimum spend required)"],"open_source":true,"self_hosted":true,"sdk_languages":["python","javascript","typescript","rust","go","java","csharp"],"frameworks":["langchain","llamaindex","vercel-ai","openai-agents","haystack","dspy"],"agent_features":{"hybrid_search":true,"metadata_filtering":true,"multi_tenancy":true,"managed_embeddings":false,"mcp_server":true,"serverless":true},"compliance":["soc2","gdpr","hipaa"],"best_for":"High-performance vector search with rich payload filtering — Rust-written, predictable latency, and a clean self-hosted path","limitations":"No built-in embedding generation — bring your own embeddings; managed cloud is younger than Pinecone's; fewer turnkey RAG modules than Weaviate","verified_by":"editorial","last_verified":"2026-06-10","source_urls":{"docs":"https://qdrant.tech/documentation","pricing":"https://qdrant.tech/pricing","changelog":"https://github.com/qdrant/qdrant/releases"},"feature_labels":{"hybrid_search":"Combined dense vector + sparse/keyword (BM25) search in one query","metadata_filtering":"Pre/post filtering by structured metadata — JSON payloads, tags, dates","multi_tenancy":"Native namespace or tenant isolation for per-user or per-agent vector spaces","managed_embeddings":"Built-in embedding generation at ingest and query time — no separate embedding service required","mcp_server":"Official Model Context Protocol server exposing vector operations as agent tools","serverless":"Pay-per-use scaling with separated storage and compute"},"comparisons":[{"slug":"chroma-vs-qdrant","title":"Chroma vs Qdrant","vs":"chroma"},{"slug":"elasticsearch-vs-qdrant","title":"Elasticsearch vs Qdrant","vs":"elasticsearch"},{"slug":"milvus-vs-qdrant","title":"Milvus vs Qdrant","vs":"milvus"},{"slug":"mongodb-atlas-vs-qdrant","title":"MongoDB Vector Search vs Qdrant","vs":"mongodb-atlas"},{"slug":"pgvector-vs-qdrant","title":"pgvector vs Qdrant","vs":"pgvector"},{"slug":"pinecone-vs-qdrant","title":"Pinecone vs Qdrant","vs":"pinecone"},{"slug":"qdrant-vs-weaviate","title":"Qdrant vs Weaviate","vs":"weaviate"}],"body":"# Qdrant\n\nQdrant is an open-source vector database written in Rust, optimized for predictable latency and rich payload filtering. Its query planner uses payload indexes to short-circuit vector search whenever a metadata filter would dominate — which makes it especially strong for agent workloads where every query is scoped by tenant, user, or document type.\n\nFor agents, Qdrant's standout features are dense + sparse + late-interaction hybrid retrieval in a single API, tenant-aware payload filtering, and shard-key-based multi-tenancy that scales horizontally. The official Qdrant MCP server exposes search, upsert, and snapshot operations as agent tools.\n\nQdrant deliberately stays out of the embedding business — you bring your own embeddings (OpenAI, Cohere, local models). For teams that want the embedding pipeline owned outside the database (versioning, replays, A/B tests), this is a feature, not a missing one. For teams that want one-click ingest of raw text, Weaviate or Pinecone are a closer fit.\n\n**Agent-specific features:**\n- Hybrid search: dense + sparse + ColBERT-style late interaction in a single `query_points` call\n- Payload-aware query planner: heavy filters short-circuit the vector search\n- Shard-key multi-tenancy with per-tenant snapshots and deletion\n- Official MCP server (`mcp-server-qdrant`) for agent-driven retrieval\n- Hybrid Cloud and Private Cloud for fully managed deployments inside your VPC"}