Qdrant vs LanceDB

Qdrant9.3Visit LanceDB8.7Visit

Higher score Qdrant #2 in Vector Database Hosting 9.3/10 Free plan Free plan✓ 2 of 3 features Visit Qdrant

vs

LanceDB #7 in Vector Database Hosting 8.7/10 Open source ✓ 2 of 3 features Visit LanceDB

• Qdrant fits best

  Open-source vector search at scale

• LanceDB fits best

  Open-source multimodal data teams

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Side by side

Feature	Qdrant 9.3/10 Visit ↗	LanceDB 8.7/10 Visit ↗
At a glance
Editor score	9.3	8.7
Ranking	#2 in Vector Database Hosting	#7 in Vector Database Hosting
Best for	Open-source vector search at scale	Open-source multimodal data teams
Pricing model	Free plan + paid	Free
Starting price	—	Open source
Free plan	✓ (best)	Not checked yet
Free trial	—	—
Deployment	Cloud, Self-hosted	Cloud, Self-hosted
Support	Email, Tickets, Community, Docs	Community, Docs
Compliance	SOC 2, HIPAA, GDPR	SOC 2, GDPR, HIPAA
Built for	Small business, Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features Qdrant 2/3 · LanceDB 2/3
Hybrid search	✓	✓
Metadata filtering	✓	✓
Serverless scaling	Not checked yet	Not checked yet
Specs
Included storage	Not checked yet	Not checked yet
Deployment regions	Not checked yet	Not checked yet
Maximum dimensions	65535 dimensions	Not checked yet
Our review
Pros	Supports dense, sparse, hybrid, and multivector retrieval Offers self-hosted and managed cloud deployment Includes REST, gRPC, and multiple language SDKs	Combines vector, full-text, and hybrid search with reranking Stores vectors, metadata, and multimodal data in shared tables Supports versioning, reproducibility, schema evolution, and filtering
Cons	The Free Tier is limited to a single-node cluster Free resources are limited to 0.5 vCPU, 1 GB RAM, and 4 GB disk SSO and private VPC links are reserved for the Premium Tier	Sparse-vector support is not part of the listed capability set Paid plan amounts are not published in the available pricing details Production deployment options add complexity beyond local use
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 stores dense, sparse, and multivector representations… Read the review →	LanceDB is an open-source embedded retrieval library and multimodal lakehouse for AI and machine-learning workloads. It is designed for teams that need to store vectors, metadata, and multimodal data together while supporting vector… Read the review →

Strengths and trade-offs

• Qdrant — where it wins

  • Supports dense, sparse, hybrid, and multivector retrieval
  • Offers self-hosted and managed cloud deployment
  • Includes REST, gRPC, and multiple language SDKs

  Where it doesn't

  • The Free Tier is limited to a single-node cluster
  • Free resources are limited to 0.5 vCPU, 1 GB RAM, and 4 GB disk
  • SSO and private VPC links are reserved for the Premium Tier

• LanceDB — where it wins

  • Combines vector, full-text, and hybrid search with reranking
  • Stores vectors, metadata, and multimodal data in shared tables
  • Supports versioning, reproducibility, schema evolution, and filtering

  Where it doesn't

  • Sparse-vector support is not part of the listed capability set
  • Paid plan amounts are not published in the available pricing details
  • Production deployment options add complexity beyond local use

• Qdrant9.3/10 · Free plan

  A flexible vector database for semantic, hybrid, and filtered search at scale.

  Visit QdrantFull verdict →
• LanceDB8.7/10 · Open source

  A strong fit for open-source AI teams building multimodal retrieval with versioned tables.

  Visit LanceDBFull verdict →