Qdrant vs Weaviate
Side-by-side comparison of two AI agent tools
Short answer
- Qdrant is growing faster: +796 GitHub stars in the last 30 days vs +152 for Weaviate.
- Pick Qdrant for: vector similarity search engine and database written in Rust, with payload filtering and managed cloud service. Pick Weaviate for: open-source cloud-native vector database for semantic search, filtering, RAG, and reranking.
From GitHub data refreshed daily.
Qdrantopen-source
Vector similarity search engine and database written in Rust, with payload filtering and managed cloud service
Weaviateopen-source
Open-source cloud-native vector database for semantic search, filtering, RAG, and reranking
Metrics
| Qdrant | Weaviate | |
|---|---|---|
| Stars | 34.9k | 16.9k |
| Star velocity /mo | 796.031746031746 | 152.06349206349208 |
| Commits (90d) | 754 | 3.8k |
| Releases (6m) | 6 | 10 |
| Overall score | 0.7393632189897725 | 0.7963911805875209 |
Pros
- +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
- +Unified query interface that combines vector similarity search with structured filtering and RAG capabilities
- +Multiple deployment options including Docker, Kubernetes, cloud services, and major cloud marketplaces (AWS, GCP)
- +Enterprise-ready with built-in multi-tenancy, replication, RBAC authorization, and integration with popular ML model providers
Cons
- -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
- -Requires understanding of vector embeddings and semantic search concepts for optimal implementation
- -May involve complexity overhead for simple use cases that don't require vector search capabilities
Use Cases
- •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
- •Building RAG (Retrieval-Augmented Generation) systems for AI chatbots and knowledge bases
- •Implementing semantic and image search functionality for content discovery applications
- •Creating recommendation engines that understand content similarity beyond keyword matching
FAQ
- Which is more popular, Qdrant or Weaviate?
- Qdrant has more GitHub stars (34,908 vs 16,861).
- Which is more actively developed, Qdrant or Weaviate?
- Weaviate had more commits in the last 90 days (3,786 vs 754).
- Should I use Qdrant or Weaviate?
- 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.