qdrant

Qdrant addresses the challenge of efficiently managing and searching through large-scale vector data, which is crucial for AI-driven applications. ML and AI teams, particularly those focused on machine learning operations (MLOps), benefit significantly from its high-performance capabilities. With a maturity level suitable for production use, Qdrant has gained 1,749 stars (6.5%) over the past 96 days, indicating strong community adoption and trust. However, it may not be the right choice for teams needing simple key-value storage or those with minimal vector search requirements.

Qdrant is a strong choice when teams require a scalable and efficient vector database for AI applications, especially in areas like image search and similarity embeddings. Alternatives should be considered if the project demands simpler database solutions or if the team lacks the expertise to implement and manage a more complex system.

This open source tool for engineering teams is particularly useful for data scientists, machine learning engineers, and AI researchers. It typically integrates into products and systems that involve advanced search capabilities, such as recommendation engines and intelligent data retrieval systems.

Latest commit: 2026-02-13. Over the past 97 days, this repository gained 1.7k stars (+6.5% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.