Milvus vs Qdrant

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.

Where Milvus wins

• Widest selection of ANN index types. HNSW, IVF_FLAT, IVF_PQ, DiskANN, SCANN, GPU-CAGRA. Qdrant is HNSW-only with quantization variants.

• Distributed architecture proven at billion-vector scale. Milvus's separation of compute, storage, and coordination is built for corpora Qdrant clusters would struggle with.

• Built-in embedding functions via pymilvus[model]. Qdrant deliberately stays out of embeddings.

Where Qdrant wins

• 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.

• Single-binary deployment vs. distributed cluster. Qdrant runs as one Rust binary. Production Milvus requires etcd, MinIO/S3, and Pulsar/Kafka.

• First-party MCP server. mcp-server-qdrant exposes search, upsert, and snapshot operations. Milvus does not yet ship a first-party MCP server.

The agentic difference

For 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.

When to pick which

• Pick Milvus for billion-vector workloads, index choice beyond HNSW, or built-in embedding functions.

• Pick Qdrant when filter-heavy queries dominate, you want lighter ops, or you need a first-party MCP server.