{"title":"Milvus vs Qdrant","slug":"milvus-vs-qdrant","tools":[{"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":"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":"Milvus and Qdrant are both open-source, purpose-built vector databases with managed cloud offerings. Milvus targets billion-vector distributed workloads; Qdrant prioritizes operational simplicity and filter-driven query planning.\n\n## Where Milvus wins\n\n* **Widest selection of ANN index types.** HNSW, IVF_FLAT, IVF_PQ, DiskANN, SCANN, GPU-CAGRA. Qdrant is HNSW-only with quantization variants.\n\n* **Distributed architecture proven at billion-vector scale.** Milvus's separation of compute, storage, and coordination is built for corpora Qdrant clusters would struggle with.\n\n* **Built-in embedding functions via `pymilvus[model]`.** Qdrant deliberately stays out of embeddings.\n\n## Where Qdrant wins\n\n* **Payload-aware query planner.** Qdrant indexes payload fields (keyword, integer, geo, datetime, UUID) and uses filter selectivity to drive the plan. Milvus filters work but lack the same planner depth.\n\n* **Single-binary deployment vs. distributed cluster.** Qdrant runs as one Rust binary. Production Milvus requires etcd, MinIO/S3, and Pulsar/Kafka.\n\n* **First-party MCP server.** `mcp-server-qdrant` exposes search, upsert, and snapshot operations. Milvus does not yet ship a first-party MCP server.\n\n## The agentic difference\n\nFor agents whose every query is heavily filtered by tenant or metadata, Qdrant's payload planner consistently beats vector-first engines on latency. For agents over corpora measured in hundreds of millions to billions of vectors, Milvus's index choice and distributed scale are necessary. Qdrant's first-party MCP server is a real advantage for agent integration today.\n\n## When to pick which\n\n* **Pick Milvus** for billion-vector workloads, index choice beyond HNSW, or built-in embedding functions.\n\n* **Pick Qdrant** when filter-heavy queries dominate, you want lighter ops, or you need a first-party MCP server."}