Qdrant
by Community
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
OSS
Qdrant
Added 1 June 2026
Overview
Qdrant is a vector database and search engine written in Rust, designed for storing and querying high-dimensional embeddings at scale. It provides similarity search capabilities for AI applications and supports both self-hosted and cloud deployment options.
Best for
Best for
Builders needing fast, scalable vector search for embeddings in production AI systems
Use cases
- Semantic search over document embeddings
- Recommendation systems based on vector similarity
- RAG pipeline vector storage and retrieval
Notes
Qdrant is a vector database and search engine written in Rust, designed for storing and querying high-dimensional embeddings at scale. It provides similarity search capabilities for AI applications and supports both self-hosted and cloud deployment options.
31,735 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.
Use cases
- Semantic search over document embeddings
- Recommendation systems based on vector similarity
- RAG pipeline vector storage and retrieval
Pros
- High performance written in Rust with low latency
- Handles massive scale with efficient indexing
- Open source with active community (31k+ stars)
Cons
- Requires operational overhead for self-hosted deployments
- Learning curve for vector database concepts and tuning
- Ecosystem smaller than established SQL databases
Indexed from awesome-llmops and enriched against its public facts.
Pros
- High performance written in Rust with low latency
- Handles massive scale with efficient indexing
- Open source with active community (31k+ stars)
Cons
- Requires operational overhead for self-hosted deployments
- Learning curve for vector database concepts and tuning
- Ecosystem smaller than established SQL databases
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