qdrant

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/

34.2k

Stars

+7.2k

Gained

26.5%

Growth

Rust

Language

View on GitHub → ↑0.4% this week

💡 Why It Matters

Qdrant addresses the need for a high-performance vector database, enabling ML/AI teams to efficiently manage and search through vast amounts of data. With its impressive 26.5% growth over 288 days, this production-ready solution is gaining traction among engineers looking for reliable tools. It is particularly beneficial for roles focused on AI search, embeddings similarity, and hybrid search applications. However, it may not be the best fit for teams with simpler data storage needs or those requiring extensive relational database features. Overall, Qdrant offers a mature, self-hosted option for teams ready to leverage advanced search capabilities.

🎯 When to Use

This is a strong choice when teams need to implement scalable vector search capabilities in AI applications. Consider alternatives if your project requires traditional database functionalities or has lower performance demands.

👥 Team Fit & Use Cases

Qdrant is ideal for data scientists, machine learning engineers, and software developers working on AI-driven products. It is commonly integrated into applications that require sophisticated search functionalities, such as recommendation systems and image retrieval platforms.

🎭 Best For

Machine Learning and AI Engineer

🏷️ Topics & Ecosystem

ai-search ai-search-engine embeddings-similarity hnsw hybrid-search image-search knn-algorithm machine-learning mlops nearest-neighbor-search neural-network neural-search recommender-system search search-engine search-engines similarity-search vector-database vector-search vector-search-engine

📊 Activity

Latest commit: 2026-08-25. Over the past 287 days, this repository gained 7.2k stars (+26.5% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.