{"title":"pgvector vs Qdrant","slug":"pgvector-vs-qdrant","tools":[{"name":"pgvector","slug":"pgvector","category":"vectordb","type":"self-hosted","website":"https://github.com/pgvector/pgvector","pricing":"open-source","pricing_tiers":["Free (open-source PostgreSQL extension)","Available on managed Postgres: Supabase, Neon, RDS, Cloud SQL, Crunchy Bridge"],"open_source":true,"self_hosted":true,"sdk_languages":["python","javascript","typescript","go","java","ruby","rust","csharp","php"],"frameworks":["langchain","llamaindex","vercel-ai","haystack"],"agent_features":{"hybrid_search":true,"metadata_filtering":true,"multi_tenancy":false,"managed_embeddings":false,"mcp_server":false,"serverless":false},"compliance":["soc2","hipaa","gdpr","pci-dss"],"best_for":"Teams already running Postgres who want vector search without adding a new database — ACID, joins, and one operational story","limitations":"Slower at scale than purpose-built engines; no native multi-tenancy primitives beyond schemas; hybrid search and serverless depend on the host platform (Supabase, Neon, etc.), not pgvector itself","verified_by":"editorial","last_verified":"2026-06-10","source_urls":{"docs":"https://github.com/pgvector/pgvector#readme","changelog":"https://github.com/pgvector/pgvector/releases","ecosystem":"https://github.com/pgvector/pgvector#hosted-postgres"}},{"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"}}],"category":"vectordb","last_verified":"2026-06-10","body":"pgvector and Qdrant offer two answers to \"where should embeddings live?\" — alongside your relational data in Postgres, or in a dedicated Rust-built vector engine. They appeal to different teams.\n\n## Where pgvector wins\n\n* **One database, one backup, one auth story.** Vectors next to users, documents, and audit logs. ACID transactions and joins work as expected.\n\n* **Row-level security enforces multi-tenancy in the database.** Qdrant scopes by payload filters or shard keys — both work, but enforcement is application-side.\n\n* **Available on every managed Postgres.** Supabase, Neon, RDS, Cloud SQL, Crunchy Bridge. No new vendor relationship needed.\n\n## Where Qdrant wins\n\n* **Payload-aware query planner.** Qdrant indexes payload fields and lets filter selectivity drive the plan. pgvector relies on Postgres's general planner, which is not vector-aware.\n\n* **Higher throughput per core at steady-state.** Rust engine purpose-built for vector workloads; pgvector inherits Postgres's general-purpose tradeoffs.\n\n* **First-party MCP server.** `mcp-server-qdrant` ships out of the box. pgvector relies on community wrappers.\n\n* **Hybrid retrieval with dense + sparse + late interaction.** Built into the engine. pgvector hybrid is hand-built with `tsvector`.\n\n## The agentic difference\n\nFor agents already reading and writing Postgres data, pgvector keeps retrieval inside the same transactional and RLS-enforced boundary. For agents whose retrieval workload is large, filter-heavy, or latency-critical, Qdrant's purpose-built planner and Rust performance deliver consistently lower p99 latency. Qdrant's first-party MCP server is a real advantage for agent integration today.\n\n## When to pick which\n\n* **Pick pgvector** when Postgres is already the operational store and you want vectors inside the same database, backup, and security model.\n\n* **Pick Qdrant** when filter-heavy queries dominate, performance matters, or you need a first-party MCP server."}