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Qdrant vs Milvus

QdrantMilvus

Bottom line: Qdrant for cost-sensitive teams wanting performance; Milvus for teams operating at large scale.

QdrantQdrant

High-performance open-source vector search engine

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MilvusMilvus

Open-source vector database built for scale

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Votes00
PricingFreemiumFreemium
CategoryCodingCoding
Tags
vector-databaseopen-sourcerustsimilarity-searchrag
vector-databaseopen-sourcesimilarity-searchscalabilityrag
Best for
  • Cost-sensitive teams wanting performance
  • RAG apps needing advanced filtering
  • Teams comfortable with self-hosting
  • Teams operating at large scale
  • Billion-vector search workloads
  • Enterprise RAG and search
Pros
  • Open source under Apache 2.0, free to self-host
  • Fast, memory-efficient Rust engine
  • Advanced metadata filtering and payload support
  • Quantization to reduce memory and cost
  • Resource-based hourly cloud billing is predictable
  • Open source under Apache 2.0, free to self-host
  • Proven at billion-vector scale
  • Distributed, cloud-native architecture
  • Multiple index types and GPU acceleration
  • Hybrid search and rich filtering
Cons
  • Self-hosting distributed clusters needs ops effort
  • Fewer built-in RAG conveniences than Weaviate
  • Smaller enterprise track record than incumbents
  • Advanced tuning requires understanding of ANN internals
  • No native embedding generation (bring your own)
  • Operationally heavy to self-host at scale
  • Multi-component architecture adds complexity
  • Overkill for small or simple projects
  • Steeper learning curve than embedded databases
  • Zilliz Cloud compute-unit pricing needs modeling

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