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Quadrant, an open source, high-performance vector search engine, offers a convenient and easy-to-use API for developers to store, manage, and search high-dimensional vectors. Its goal is to transform embeddings or neural network encoders into applications connected to recommender systems, image retrieval, natural language processing, and image or text classification.
Written in Rust, Qdrant provides fast and reliable performance for vector similarity search needs. During two years of development, it was equipped with high scalability, advanced filtering support, and more useful features.
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Benchmark-tested, high-performance vector search database
The Qdrant team has compared several open source search engines and vector databases using different configurations on three data sets with different functions of distance and vector dimensionality. The benchmarks take into account the impact of different configuration parameters for both the search engine and the search operation, as well as the effect of the number of search threads on performance.
According to the reference point, Qdrant is one of the fastest engines in terms of indexing time and builds internal search structures much faster than its competitors. Qdrant consistently achieves the highest RPS (requests per second) and lowest latencies in most scenarios, regardless of the precision threshold and metric used.
read this for more detailed calculations and a link to the open source reference repository.
Managed cloud platform
Recently Qdrant was released in its major version 1.0 and now it is the fast and efficient vector search engine available in the cloud for enterprise business use. The managed cloud platform gives businesses the benefits of advanced vector similarity search capabilities while eliminating implementation and maintenance overhead.
It ensures high availability and performance for users, as well as expert support to help resolve any issues that may arise. Additionally, there is a free tier available for users to test the service and see if it suits their needs before committing to a paid plan.
The record is available at this link.
