{"title":"Qdrant vs Weaviate","slug":"qdrant-vs-weaviate","tools":[{"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"}},{"name":"Weaviate","slug":"weaviate","category":"vectordb","type":"hybrid","website":"https://weaviate.io","pricing":"freemium","pricing_tiers":["Free (self-hosted, open-source)","Free Hosted","$45/mo Flex","$280/mo Plus","$400/mo Premium"],"open_source":true,"self_hosted":true,"sdk_languages":["python","javascript","typescript","go","java"],"frameworks":["langchain","llamaindex","vercel-ai","haystack","dspy"],"agent_features":{"hybrid_search":true,"metadata_filtering":true,"multi_tenancy":true,"managed_embeddings":true,"mcp_server":true,"serverless":true},"compliance":["soc2","gdpr","hipaa"],"best_for":"Hybrid RAG with built-in vectorizer modules, generative search, and strong multi-tenancy — runs anywhere from laptop to managed cloud","limitations":"Resource-hungry compared to Qdrant or Chroma; module configuration has a learning curve; some advanced replication features are cloud-only","verified_by":"editorial","last_verified":"2026-06-10","source_urls":{"docs":"https://weaviate.io/developers/weaviate","pricing":"https://weaviate.io/pricing","changelog":"https://github.com/weaviate/weaviate/releases"}}],"category":"vectordb","last_verified":"2026-06-10","body":"Qdrant and Weaviate are the two most-considered open-source vector databases for production agent workloads. Both ship hybrid search, multi-tenancy, and MCP servers, but they prioritize different things: Qdrant favors raw performance and filter-driven querying, Weaviate favors module ecosystem and tenant lifecycle.\n\n## Where Qdrant wins\n\n* **Payload-aware query planner.** Qdrant indexes payload fields (keyword, integer, geo, datetime, and UUID) and uses filter selectivity to drive the plan. Weaviate filters work but lack the same planner depth.\n\n* **Lighter footprint.** Rust binary with low memory baseline. Weaviate is heavier per node.\n\n* **Higher throughput per core at steady-state.** Independent benchmarks consistently put Qdrant near the top on QPS-per-core for filtered vector queries.\n\n## Where Weaviate wins\n\n* **Generative modules run RAG inside the database.** `generative-*` retrieves + calls an LLM in one query. Qdrant retrieves only.\n\n* **Tenant-per-collection scales to 100k+ tenants with offloading.** First-class `ACTIVE` / `INACTIVE` / `OFFLOADED` tenant states. Qdrant uses shard keys, which are powerful but flatter.\n\n* **Built-in vectorizer modules.** OpenAI, Cohere, Voyage, Hugging Face, Ollama at ingest time. Qdrant deliberately stays out of embedding generation.\n\n## The agentic difference\n\nFor agents whose every query is heavily filtered — tenant, doc type, date — Qdrant's payload planner consistently delivers lower p99 latency. For agents with long-tail multi-tenant patterns or where you want generation inside the database, Weaviate's tenant model and generative modules pull ahead. Both ship first-party MCP servers and integrate cleanly with major agent frameworks; the differentiator is whether you want the database to do more (Weaviate) or stay narrowly fast (Qdrant).\n\n## When to pick which\n\n* **Pick Qdrant** when filter-heavy queries dominate, you want vector-first performance per dollar, or you prefer to own chunking and embedding outside the database.\n\n* **Pick Weaviate** when you need first-class multi-tenancy at scale, built-in vectorizer and generative modules, or retrieval + generation inside the database."}