Milvus vs Qdrant
Side-by-side comparison of two AI agent tools
Short answer
- Qdrant is growing faster: +796 GitHub stars in the last 30 days vs +444 for Milvus.
- Pick Milvus for: milvus is a high-performance, cloud-native vector database built for scalable vector ANN search. Pick Qdrant for: vector similarity search engine and database written in Rust, with payload filtering and managed cloud service.
From GitHub data refreshed daily.
Milvusopen-source
Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search
Qdrantopen-source
Vector similarity search engine and database written in Rust, with payload filtering and managed cloud service
Metrics
| Milvus | Qdrant | |
|---|---|---|
| Stars | 46.3k | 34.9k |
| Star velocity /mo | 444.1269841269841 | 796.031746031746 |
| Commits (90d) | 681 | 754 |
| Releases (6m) | 10 | 6 |
| Overall score | 0.8182449811798933 | 0.7393632189897725 |
Pros
- +硬件加速优化：内置 CPU/GPU 加速和分布式架构，在数十亿向量规模下提供业界顶级的搜索性能
- +灵活的部署选择：从轻量级的 Milvus Lite 到企业级分布式集群，再到云端全托管服务，满足不同规模需求
- +实时数据更新：支持流式数据更新和 Kubernetes 原生架构，确保 AI 应用数据的实时性和可扩展性
- +High-performance Rust implementation delivers fast vector operations and reliable performance under heavy loads with proven benchmarks
- +Advanced filtering capabilities allow complex queries combining vector similarity with metadata filtering for sophisticated search scenarios
- +Production-ready with both self-hosted and managed cloud options, including comprehensive APIs and client libraries for easy integration
Cons
- -学习曲线较陡：需要深入理解向量嵌入、相似性搜索和分布式系统概念才能有效使用
- -资源消耗较大：大规模部署时对计算和存储资源要求较高，运维成本相对较大
- -配置复杂性：分布式架构的配置和调优需要专业知识，对小型项目可能过于复杂
- -Specialized focus on vector operations means additional tools needed for traditional database operations and non-vector data storage
- -Requires understanding of vector embeddings and similarity search concepts, creating a learning curve for teams new to vector databases
Use Cases
- •大规模语义搜索：构建企业级文档检索系统，支持自然语言查询和语义相似度匹配
- •图像视频相似性检索：电商产品推荐、内容审核、多媒体资产管理等场景的视觉搜索
- •个性化推荐系统：基于用户行为向量和物品特征向量构建实时推荐引擎
- •Semantic search applications that need to find similar documents, images, or content based on meaning rather than exact keywords
- •Recommendation systems that match user preferences with product catalogs or content libraries using neural network embeddings
- •Neural network-based matching for applications like duplicate detection, content classification, or similarity-based grouping
FAQ
- Which is more popular, Milvus or Qdrant?
- Milvus has more GitHub stars (46,302 vs 34,904).
- Which is more actively developed, Milvus or Qdrant?
- Qdrant had more commits in the last 90 days (754 vs 681).
- Should I use Milvus or Qdrant?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.