Milvus vs Qdrant

Milvus8.8Visit Qdrant8.7Visit

Higher score Milvus #6 in Vector Databases 8.8/10 Free plan Free plan✓ 3 of 3 features Visit Milvus

vs

Qdrant #7 in Vector Databases 8.7/10 Free plan Free plan✓ 3 of 3 features Visit Qdrant

• Milvus fits best

  Open-source teams needing maximum vector depth

• Qdrant fits best

  Modern teams wanting open hybrid search

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Side by side

Feature	Milvus 8.8/10 Visit ↗	Qdrant 8.7/10 Visit ↗
At a glance
Editor score	8.8	8.7
Ranking	#6 in Vector Databases	#7 in Vector Databases
Best for	Open-source teams needing maximum vector depth	Modern teams wanting open hybrid search
Pricing model	Free	Free plan + paid
Starting price	Free plan	—
Free plan	✓	✓
Free trial	—	—
Deployment	Cloud, Self-hosted	Cloud, Self-hosted
Support	Community, Docs	Email, Tickets, Community, Docs
Built for	Solo, Small business, Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features Milvus 3/3 · Qdrant 3/3
Hybrid search	✓	✓
Metadata filtering	✓	✓
Sparse vectors	✓	✓
Specs
Deployment	Both	Both
Maximum dimensions	Not checked yet	65535 dimensions
Index types	HNSW, IVF, FLAT, DiskANN, SCANN	HNSW; sparse vector index; payload index; filterable HNSW
SDK languages	Python, Go, Java, Node.js, C++	Python, JavaScript/TypeScript, Rust, Go, .NET, Java
Our review
Pros	Supports dense, sparse, binary, hybrid, filtered, and reranked search Runs locally, with Docker, or across distributed Kubernetes deployments Offers broad index coverage and SDKs for Python, Go, Java, Node.js, and C++	Hybrid dense and sparse search with metadata filtering Open-source self-hosting plus managed cloud deployment Multivectors, named vectors, REST, and gRPC APIs
Cons	Self-hosted deployment places infrastructure and operations work on your team Its broad index catalog can make architecture decisions more involved Managed cloud is provided separately through Zilliz Cloud	The Free Tier is limited to a single node Free resources are limited to 0.5 vCPU, 1 GB RAM, and 4 GB disk Paid cloud tiers use usage-based pricing rather than fixed plan amounts
Our verdict	Milvus is an open-source vector database for GenAI applications and similarity search. It suits teams building retrieval-augmented generation, image search, multimodal search, and large-scale production systems that need more than a single… Read the review →	Qdrant is an open-source vector search engine and vector database for developers building semantic search, recommendations, retrieval-augmented generation, and other AI applications. It supports self-hosted deployment and Qdrant Cloud,… Read the review →

Strengths and trade-offs

• Milvus — where it wins

  • Supports dense, sparse, binary, hybrid, filtered, and reranked search
  • Runs locally, with Docker, or across distributed Kubernetes deployments
  • Offers broad index coverage and SDKs for Python, Go, Java, Node.js, and C++

  Where it doesn't

  • Self-hosted deployment places infrastructure and operations work on your team
  • Its broad index catalog can make architecture decisions more involved
  • Managed cloud is provided separately through Zilliz Cloud

• Qdrant — where it wins

  • Hybrid dense and sparse search with metadata filtering
  • Open-source self-hosting plus managed cloud deployment
  • Multivectors, named vectors, REST, and gRPC APIs

  Where it doesn't

  • The Free Tier is limited to a single node
  • Free resources are limited to 0.5 vCPU, 1 GB RAM, and 4 GB disk
  • Paid cloud tiers use usage-based pricing rather than fixed plan amounts

• Milvus8.8/10 · Free plan

  A deep, flexible open-source choice for demanding vector and hybrid search workloads.

  Visit MilvusFull verdict →
• Qdrant8.7/10 · Free plan

  A flexible vector database for teams building hybrid search and AI retrieval.

  Visit QdrantFull verdict →