qdrant/qdrant is a vector database written in Rust. It takes care of storing the embeddings that AI applications work with — that is, representations of data turned into arrays of numbers — and finding similar ones. The repository description presents the project as a high-performance, large-scale vector similarity search engine for next-generation AI applications. The latest release found in this repository is v1.19.2, sourced from the qdrant/qdrant repository on GitHub. However, the given materials contain nothing about what changed in this release.
The README states that Qdrant is a real database implementation that stores, searches and manages vector points with payloads (metadata) through an API. Search supports extended filtering, which narrows the scope by combining conditions with vector similarity, and hybrid search, which mixes several search methods. Deployment comes in two forms: Qdrant Edge, the lightweight version, and fully managed Qdrant Cloud, which includes a free tier. The primary language is Rust, the license is Apache-2.0, and the GitHub star count is 35165.
But the verifiable scope ends here. The record contains no performance figures or benchmarks, and no comparison material with other vector databases. The high-performance and large-scale wording in the repository description is the project's own claim, not a fact confirmed by measurement. Of the score items, only momentum is present, at 5.0 out of 10, while the novelty, technology and quality scores are empty. The creation date is confirmed as May 30, 2020. Since there is no basis within this record to gauge scale and performance, it makes sense to first work out whether the features you need fall within this scope.