Qdrant provides an open-source vector search database that enables high-performance similarity searches for high-dimensional data, facilitating the development of applications for matching, searching, and recommending. By efficiently processing embeddings from neural networks, Qdrant allows businesses to leverage unstructured data for enhanced data analysis and personalized user experiences.
Funding
$37.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Many modern applications require the ability to perform similarity searches on high-dimensional data, such as embeddings generated by neural networks, but traditional databases are not optimized for this type of workload. This makes it difficult to efficiently implement features like recommendation systems, advanced search, and anomaly detection.
Solution
Qdrant is an open-source vector search database designed for high-performance similarity searches at scale. It efficiently stores and processes high-dimensional vectors, enabling developers to build AI-powered applications that leverage unstructured data. Qdrant offers features like cloud-native scalability, built-in compression, and a Rust-powered core for speed and reliability. It integrates with leading embedding models and frameworks, allowing users to easily turn embeddings into applications for matching, searching, recommending, and more.
Target Audience
Qdrant's primary users are AI/ML engineers, data scientists, and software developers building applications that require vector similarity search, such as recommendation systems, semantic search engines, and anomaly detection tools.
Features
- High-dimensional vector storage and indexing
- Optimized for similarity search and nearest neighbor queries
- Cloud-native architecture for scalability and high availability
- Built-in compression options to reduce memory usage
- Rust-based core for performance and reliability
- Support for various distance metrics (e.g., cosine, Euclidean)
- Integration with popular machine learning frameworks
- Recommendation API with flexible scoring strategies
- Payload filtering for retrieval-augmented generation