Qdrant vs Zilliz Cloud

Qdrant8.7Visit Zilliz Cloud8.6Visit

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

vs

Zilliz Cloud #8 in Vector Databases 8.6/10 Free plan · 30-day trial Free planFree trial✓ 3 of 3 features Visit Zilliz Cloud

• Qdrant fits best

  Modern teams wanting open hybrid search

• Zilliz Cloud fits best

  Managed Milvus-compatible cloud deployments

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Side by side

Feature	Qdrant 8.7/10 Visit ↗	Zilliz Cloud 8.6/10 Visit ↗
At a glance
Editor score	8.7	8.6
Ranking	#7 in Vector Databases	#8 in Vector Databases
Best for	Modern teams wanting open hybrid search	Managed Milvus-compatible cloud deployments
Pricing model	Free plan + paid	Free plan + paid
Starting price	—	—
Free plan	✓	✓
Free trial	—	30 days
Deployment	Cloud, Self-hosted	Cloud
Platforms	Web, Windows, macOS, Linux	Web
Support	Email, Tickets, Community, Docs	Docs
Compliance	SOC 2, HIPAA, GDPR	SSO/SAML, 2FA, GDPR, HIPAA, SOC 2, ISO 27001
Built for	Small business, Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features Qdrant 3/3 · Zilliz Cloud 3/3
Hybrid search	✓	✓
Metadata filtering	✓	✓
Sparse vectors	✓	✓
Specs
Deployment	Both	Cloud
Maximum dimensions	65535 dimensions (best)	32768 dimensions
Index types	HNSW; sparse vector index; payload index; filterable HNSW	FLAT, IVF_FLAT, IVF_SQ8, IVF_PQ, HNSW, DISKANN, AUTOINDEX, SCANN, HNSW_SQ, HNSW_PQ, HNSW_PRQ, IVF_RABITQ, SPARSE_INVERTED_INDEX, SPARSE_WAND
SDK languages	Python, JavaScript/TypeScript, Rust, Go, .NET, Java	Python, Java, Go, Node.js, C++
Our review
Pros	Hybrid dense and sparse search with metadata filtering Open-source self-hosting plus managed cloud deployment Multivectors, named vectors, REST, and gRPC APIs	Supports dense, sparse, binary, hybrid, and full-text search Includes filtering, reranking, backups, restore, and migration tools Offers SaaS and BYOC deployment with major cloud storage integrations
Cons	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	Paid tiers use usage-based pricing rather than fixed subscription rates Cloud deployment may not suit teams seeking a self-hosted product Its broad feature set may exceed the needs of simple vector search projects
Our verdict	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 →	Zilliz Cloud is a fully managed Milvus service for building and operating vector search applications. It is aimed at teams developing semantic search, retrieval-augmented generation, recommendations, and multimodal retrieval workloads… Read the review →

Strengths and trade-offs

• 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

• Zilliz Cloud — where it wins

  • Supports dense, sparse, binary, hybrid, and full-text search
  • Includes filtering, reranking, backups, restore, and migration tools
  • Offers SaaS and BYOC deployment with major cloud storage integrations

  Where it doesn't

  • Paid tiers use usage-based pricing rather than fixed subscription rates
  • Cloud deployment may not suit teams seeking a self-hosted product
  • Its broad feature set may exceed the needs of simple vector search projects

• Qdrant8.7/10 · Free plan

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

  Visit QdrantFull verdict →
• Zilliz Cloud8.6/10 · Free plan · 30-day trial

  A capable managed vector database for teams building AI search applications.

  Visit Zilliz CloudFull verdict →