{"category":"vectordb","title":"Vector Databases for AI Agents","description":"Compare vector databases for RAG, semantic search, and agent memory","tools":[{"name":"Pinecone","slug":"pinecone","category":"vectordb","type":"cloud","website":"https://www.pinecone.io","pricing":"freemium","pricing_tiers":["Free (1 starter index, 2GB storage)","$50/mo Standard (usage-based)","Custom Enterprise"],"open_source":false,"self_hosted":false,"sdk_languages":["python","javascript","typescript","go","java"],"frameworks":["langchain","llamaindex","vercel-ai","openai-agents","haystack"],"agent_features":{"hybrid_search":true,"metadata_filtering":true,"multi_tenancy":true,"managed_embeddings":true,"mcp_server":true,"serverless":true},"compliance":["soc2","hipaa","gdpr","iso27001"],"best_for":"Production RAG and agent memory at scale — serverless billing, generous free tier, and a managed experience with zero ops","limitations":"Cloud-only with no self-hosted option; vendor lock-in on proprietary index format; advanced features like dedicated read nodes add cost quickly at scale","verified_by":"editorial","last_verified":"2026-06-10","source_urls":{"docs":"https://docs.pinecone.io","pricing":"https://www.pinecone.io/pricing","changelog":"https://docs.pinecone.io/release-notes"}},{"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"}},{"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":"Milvus","slug":"milvus","category":"vectordb","type":"hybrid","website":"https://milvus.io","pricing":"freemium","pricing_tiers":["Free (self-hosted, Apache 2.0)","Free Zilliz Cloud Serverless tier","$126/GB/mo Standard Zilliz Cloud Dedicated","$197/mo Enterprise","Business Critical"],"open_source":true,"self_hosted":true,"sdk_languages":["python","javascript","typescript","go","java","csharp","ruby"],"frameworks":["langchain","llamaindex","vercel-ai","haystack","dspy"],"agent_features":{"hybrid_search":true,"metadata_filtering":true,"multi_tenancy":true,"managed_embeddings":true,"mcp_server":false,"serverless":true},"compliance":["soc2","gdpr","hipaa","iso27001"],"best_for":"Billion-scale vector workloads with the widest selection of ANN index types — HNSW, IVF, DiskANN, SCANN, GPU indexes","limitations":"Self-hosted deployment is operationally complex (depends on etcd, MinIO, Pulsar/Kafka); no first-party MCP server yet; managed offering is through Zilliz, not Milvus directly","verified_by":"editorial","last_verified":"2026-06-10","source_urls":{"docs":"https://milvus.io/docs","pricing":"https://zilliz.com/pricing","changelog":"https://github.com/milvus-io/milvus/releases"}},{"name":"Chroma","slug":"chroma","category":"vectordb","type":"hybrid","website":"https://www.trychroma.com","pricing":"freemium","pricing_tiers":["Free (self-hosted, Apache 2.0)","Starter ($0/mo + usage)","Team ($250/mo + usage)","Custom Enterprise"],"open_source":true,"self_hosted":true,"sdk_languages":["python","javascript","typescript","ruby"],"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"],"best_for":"AI-native developer experience — the fastest path from `pip install` to a working RAG prototype, with a managed cloud when you're ready","limitations":"Younger than Pinecone or Weaviate at scale; fewer index tuning knobs (HNSW only); compliance certifications are still maturing","verified_by":"editorial","last_verified":"2026-06-10","source_urls":{"docs":"https://docs.trychroma.com","pricing":"https://www.trychroma.com/pricing","changelog":"https://github.com/chroma-core/chroma/releases"}},{"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":"MongoDB Vector Search","slug":"mongodb-atlas","category":"vectordb","type":"cloud","website":"https://www.mongodb.com/products/platform/atlas-vector-search","pricing":"freemium","pricing_tiers":["Free M0 cluster (512MB)","$57+/mo M10 dedicated","Search Nodes billed separately","Custom Enterprise"],"open_source":false,"self_hosted":false,"sdk_languages":["python","javascript","typescript","go","java","csharp","ruby","php","rust"],"frameworks":["langchain","llamaindex","vercel-ai","haystack"],"agent_features":{"hybrid_search":true,"metadata_filtering":true,"multi_tenancy":true,"managed_embeddings":true,"mcp_server":true,"serverless":true},"compliance":["soc2","hipaa","gdpr","pci-dss","iso27001"],"best_for":"Teams already on MongoDB who want vector search next to operational data — one driver, one query language, one backup story","limitations":"Vector indexes are tied to Atlas (not self-hosted Community Server); search nodes add cost; index build times can be long for large collections","verified_by":"editorial","last_verified":"2026-06-10","source_urls":{"docs":"https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/","pricing":"https://www.mongodb.com/pricing","changelog":"https://www.mongodb.com/docs/atlas/release-notes/"}},{"name":"Elasticsearch","slug":"elasticsearch","category":"vectordb","type":"hybrid","website":"https://www.elastic.co/elasticsearch","pricing":"freemium","pricing_tiers":["Free (self-hosted, Elastic License v2 / SSPL)","Elastic Cloud Hosted $99+/mo (Standard, Gold, Platinum, Enterprise)","Elasticsearch Serverless (pay per use)"],"open_source":true,"self_hosted":true,"sdk_languages":["python","javascript","typescript","go","java","ruby","php","csharp","rust"],"frameworks":["langchain","llamaindex","vercel-ai","haystack"],"agent_features":{"hybrid_search":true,"metadata_filtering":true,"multi_tenancy":true,"managed_embeddings":true,"mcp_server":true,"serverless":true},"compliance":["soc2","hipaa","gdpr","pci-dss","iso27001"],"best_for":"Hybrid search at scale on top of mature search infrastructure — combine BM25, ELSER sparse vectors, and dense vectors in one query","limitations":"Heavier and more memory-hungry than purpose-built vector engines; license is no longer pure Apache 2.0; vector-only workloads are overserved by Elasticsearch's full feature surface","verified_by":"editorial","last_verified":"2026-06-10","source_urls":{"docs":"https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html","pricing":"https://www.elastic.co/pricing","changelog":"https://www.elastic.co/docs/release-notes/elasticsearch"}}],"feature_definitions":{"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-elasticsearch","title":"Chroma vs Elasticsearch","tools":["chroma","elasticsearch"],"popular":false},{"slug":"chroma-vs-milvus","title":"Chroma vs Milvus","tools":["chroma","milvus"],"popular":false},{"slug":"chroma-vs-mongodb-atlas","title":"Chroma vs MongoDB Vector Search","tools":["chroma","mongodb-atlas"],"popular":false},{"slug":"chroma-vs-pgvector","title":"Chroma vs pgvector","tools":["chroma","pgvector"],"popular":false},{"slug":"chroma-vs-pinecone","title":"Chroma vs Pinecone","tools":["chroma","pinecone"],"popular":false},{"slug":"chroma-vs-qdrant","title":"Chroma vs Qdrant","tools":["chroma","qdrant"],"popular":false},{"slug":"chroma-vs-weaviate","title":"Chroma vs Weaviate","tools":["chroma","weaviate"],"popular":false},{"slug":"elasticsearch-vs-milvus","title":"Elasticsearch vs Milvus","tools":["elasticsearch","milvus"],"popular":false},{"slug":"elasticsearch-vs-mongodb-atlas","title":"Elasticsearch vs MongoDB Vector Search","tools":["elasticsearch","mongodb-atlas"],"popular":false},{"slug":"elasticsearch-vs-pgvector","title":"Elasticsearch vs pgvector","tools":["elasticsearch","pgvector"],"popular":false},{"slug":"elasticsearch-vs-pinecone","title":"Elasticsearch vs Pinecone","tools":["elasticsearch","pinecone"],"popular":false},{"slug":"elasticsearch-vs-qdrant","title":"Elasticsearch vs Qdrant","tools":["elasticsearch","qdrant"],"popular":false},{"slug":"elasticsearch-vs-weaviate","title":"Elasticsearch vs Weaviate","tools":["elasticsearch","weaviate"],"popular":false},{"slug":"milvus-vs-mongodb-atlas","title":"Milvus vs MongoDB Vector Search","tools":["milvus","mongodb-atlas"],"popular":false},{"slug":"milvus-vs-pgvector","title":"Milvus vs pgvector","tools":["milvus","pgvector"],"popular":false},{"slug":"milvus-vs-pinecone","title":"Milvus vs Pinecone","tools":["milvus","pinecone"],"popular":false},{"slug":"milvus-vs-qdrant","title":"Milvus vs Qdrant","tools":["milvus","qdrant"],"popular":false},{"slug":"milvus-vs-weaviate","title":"Milvus vs Weaviate","tools":["milvus","weaviate"],"popular":false},{"slug":"mongodb-atlas-vs-pgvector","title":"MongoDB Vector Search vs pgvector","tools":["mongodb-atlas","pgvector"],"popular":false},{"slug":"mongodb-atlas-vs-pinecone","title":"MongoDB Vector Search vs Pinecone","tools":["mongodb-atlas","pinecone"],"popular":false},{"slug":"mongodb-atlas-vs-qdrant","title":"MongoDB Vector Search vs Qdrant","tools":["mongodb-atlas","qdrant"],"popular":false},{"slug":"mongodb-atlas-vs-weaviate","title":"MongoDB Vector Search vs Weaviate","tools":["mongodb-atlas","weaviate"],"popular":false},{"slug":"pgvector-vs-pinecone","title":"pgvector vs Pinecone","tools":["pgvector","pinecone"],"popular":true},{"slug":"pgvector-vs-qdrant","title":"pgvector vs Qdrant","tools":["pgvector","qdrant"],"popular":false},{"slug":"pgvector-vs-weaviate","title":"pgvector vs Weaviate","tools":["pgvector","weaviate"],"popular":false},{"slug":"pinecone-vs-qdrant","title":"Pinecone vs Qdrant","tools":["pinecone","qdrant"],"popular":true},{"slug":"pinecone-vs-weaviate","title":"Pinecone vs Weaviate","tools":["pinecone","weaviate"],"popular":true},{"slug":"qdrant-vs-weaviate","title":"Qdrant vs Weaviate","tools":["qdrant","weaviate"],"popular":false}],"body":"# Vector Databases for AI Agents\n\nVector databases are the persistence layer behind retrieval-augmented generation (RAG), agent long-term memory, and semantic tool routing. Where a traditional database matches rows by exact values, a vector database matches embeddings by similarity — finding the chunks of text, code, or images that are conceptually closest to a query, even when no keyword overlaps.\n\nFor AI agents specifically, the vector store is rarely a passive lookup. Agents call it as a tool, scope queries per user or per session, filter by metadata, and combine vector relevance with keyword precision. The right database can mean the difference between an agent that grounds its answers and one that hallucinates against stale context.\n\nThe tools in this category range from purpose-built, fully managed services (Pinecone, MongoDB Vector Search) to open-source engines you can self-host (Weaviate, Qdrant, Milvus, Chroma) to extensions on databases you already run (pgvector, Elasticsearch).\n\n**What each feature means:**\n\n- **Hybrid search** — the database can combine dense vector similarity with sparse/keyword (BM25) scoring in a single query. Hybrid search reliably outperforms pure-vector search for RAG over technical or proper-noun-heavy corpora.\n- **Metadata filtering** — you can attach structured JSON payloads to each vector and constrain queries by them (tenant ID, document type, date ranges, ACLs). Critical for multi-user agent apps where leaking the wrong tenant's data is a bug, not a feature.\n- **Multi-tenancy** — first-class namespace or tenant isolation so per-user or per-agent vector spaces don't share an index. Reduces noisy-neighbor problems and simplifies per-tenant deletion.\n- **Managed embeddings** — the database generates embeddings for you at ingest and query time, removing the need for a separate embedding pipeline. Convenient for prototyping; sometimes a lock-in concern at scale.\n- **MCP server** — an official Model Context Protocol server that exposes vector operations (upsert, query, delete) as agent tools, with auth and schema baked in.\n- **Serverless** — separated storage and compute with pay-per-use billing. Matters when your agent's vector workload is bursty or long-tail across many small tenants.\n\nA `?` in the comparison table means the feature is unverified at the time of the last editorial check, not that it's absent. Check `last_verified` and follow `source_urls` to confirm current status."}