Qdrant vs Weaviate

Qdrant

High-performance vector similarity search engine

Weaviate

AI-native vector database with semantic modules

Feature Qdrant Weaviate
Category LLMs & AI Infra LLMs & AI Infra
Sub-category Vector DB Vector DB
Maturity stable stable
Complexity intermediate intermediate
Performance tier enterprise grade enterprise grade
License Apache-2.0 BSD-3-Clause
License type permissive permissive
Pricing fully free fully free
GitHub stars 22.0K 12.0K
Contributors 150 200
Commit frequency daily daily
Plugin ecosystem none none
Docs quality good good
Backing org Qdrant Weaviate
Funding model vc_backed vc_backed
Min RAM 2 GB 2 GB
Min CPU cores 2 2
Scaling pattern horizontal horizontal
Self-hostable Yes Yes
K8s native Yes Yes
Offline capable No No
Vendor lock-in none none
Languages Rust Go
API type REST, gRPC REST, gRPC
Protocols HTTP HTTP
Deployment docker, binary docker, binary
SDK languages python, javascript, go, rust python, javascript, go, rust
Team size fit solo, small, medium, enterprise solo, small, medium, enterprise
First release 2021 2021
Latest version

When to use Qdrant

  • RAG retrieval backend for LLM applications
  • Product recommendation via embedding similarity
  • Semantic document search across knowledge base
  • Image similarity search for e-commerce

When to use Weaviate

  • RAG retrieval backend for LLM applications
  • Product recommendation via embedding similarity
  • Semantic document search across knowledge base
  • Image similarity search for e-commerce

Qdrant anti-patterns

  • Not a general-purpose database
  • No SQL support
  • Needs separate storage for non-vector data

Weaviate anti-patterns

  • Heavier than Chroma for simple use cases
  • GraphQL API has learning curve
  • Module system adds complexity
Full Qdrant profile → Full Weaviate profile → All comparisons