{"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"},"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-weaviate","title":"Chroma vs Weaviate","vs":"chroma"},{"slug":"elasticsearch-vs-weaviate","title":"Elasticsearch vs Weaviate","vs":"elasticsearch"},{"slug":"milvus-vs-weaviate","title":"Milvus vs Weaviate","vs":"milvus"},{"slug":"mongodb-atlas-vs-weaviate","title":"MongoDB Vector Search vs Weaviate","vs":"mongodb-atlas"},{"slug":"pgvector-vs-weaviate","title":"pgvector vs Weaviate","vs":"pgvector"},{"slug":"pinecone-vs-weaviate","title":"Pinecone vs Weaviate","vs":"pinecone"},{"slug":"qdrant-vs-weaviate","title":"Qdrant vs Weaviate","vs":"qdrant"}],"body":"# Weaviate\n\nWeaviate is an open-source vector database with the broadest \"batteries-included\" story of any vector engine. Its module system can pull embeddings from OpenAI, Cohere, Hugging Face, or local models, and its `generative-*` modules let you run RAG generation inside the database, returning grounded answers in a single query.\n\nFor agents, the killer feature is multi-tenancy: Weaviate's tenant-per-collection model can scale to hundreds of thousands of tenants on a single cluster, with per-tenant activation and offloading to cold storage. Hybrid search (BM25 + vector with configurable alpha) is first-class, not an add-on.\n\nWeaviate publishes an official MCP server that exposes collection management and search to MCP-aware agents. The Serverless Cloud tier separates storage from compute for cost-efficient bursty workloads, and the BYOC (bring-your-own-cloud) option satisfies stricter data residency requirements without giving up the managed experience.\n\nThe tradeoff is resource appetite — Weaviate is heavier than Qdrant or Chroma in memory and CPU, and the module system rewards investment in configuration to get right.\n\n**Agent-specific features:**\n- Tenant-per-collection isolation that scales to 100k+ tenants per cluster\n- Hybrid search (BM25F + vector) with configurable fusion\n- Vectorizer and generative modules: embeddings and RAG happen inside the database\n- Official MCP server for agent-driven collection and search operations\n- Serverless Cloud with separated storage and compute, plus BYOC for data residency"}