{"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"},"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-milvus","title":"Chroma vs Milvus","vs":"chroma"},{"slug":"elasticsearch-vs-milvus","title":"Elasticsearch vs Milvus","vs":"elasticsearch"},{"slug":"milvus-vs-mongodb-atlas","title":"Milvus vs MongoDB Vector Search","vs":"mongodb-atlas"},{"slug":"milvus-vs-pgvector","title":"Milvus vs pgvector","vs":"pgvector"},{"slug":"milvus-vs-pinecone","title":"Milvus vs Pinecone","vs":"pinecone"},{"slug":"milvus-vs-qdrant","title":"Milvus vs Qdrant","vs":"qdrant"},{"slug":"milvus-vs-weaviate","title":"Milvus vs Weaviate","vs":"weaviate"}],"body":"# Milvus\n\nMilvus is the highest-scale open-source vector database, designed for billion-vector workloads. Its distributed architecture separates compute, storage, and coordination, and its index zoo (HNSW, IVF_FLAT, IVF_PQ, DiskANN, SCANN, GPU-CAGRA) is the broadest of any open-source engine.\n\nFor AI agents at scale, Milvus's strengths are partition-based multi-tenancy, hybrid search across multiple vector fields (dense + sparse + multi-vector reranking), and a managed serverless tier through Zilliz Cloud with pay-per-use billing. The integrated `pymilvus[model]` package exposes OpenAI, Cohere, Voyage, and SentenceTransformer embedders so you can ingest text directly.\n\nThe tradeoff is operational complexity. Self-hosting Milvus in production means running etcd, MinIO (or S3-compatible storage), and Pulsar or Kafka — far more moving parts than Qdrant or Chroma. Milvus Lite (single-file embedded mode) covers prototyping, but production teams typically end up on Zilliz Cloud to avoid the ops burden. Milvus does not yet ship a first-party MCP server.\n\n**Agent-specific features:**\n- Partition keys for cheap per-tenant isolation inside a single collection\n- Hybrid search with multi-vector fields and pluggable rerankers (RRF, weighted)\n- DiskANN and GPU-CAGRA indexes for billion-scale corpora that don't fit in RAM\n- Built-in embedding functions via `pymilvus[model]`\n- Serverless and dedicated tiers on Zilliz Cloud; BYOC on AWS, GCP, and Azure"}