--- title: "cuvs vs qdrant" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvidia-cuvs-vs-qdrant-qdrant" tools: ["nvidia-cuvs", "qdrant-qdrant"] --- # cuvs vs qdrant *GraphCanon updated Jul 12, 2026* ## Verdict Pick cuvs if cuVS is a CUDA-based library for efficient GPU-accelerated vector search and clustering; pick qdrant if high-performance vector database with support for distributed deployment. [cuvs](https://docs.rapids.ai/api/cuvs/stable/) reports 810 GitHub stars, 210 forks, and 645 open issues, last pushed Jul 11, 2026. [qdrant](https://qdrant.tech) has 33k stars, 2.5k forks, and 631 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [cuvs's repository](https://github.com/NVIDIA/cuvs) and [qdrant's repository](https://github.com/qdrant/qdrant). | | [cuvs](/tools/nvidia-cuvs.md) | [qdrant](/tools/qdrant-qdrant.md) | | --- | --- | --- | | Tagline | A library for vector search and clustering on the GPU | High-performance, massive-scale Vector Database and Vector Search Engine | | Stars | 810 | 33,143 | | Forks | 210 | 2,483 | | Open issues | 645 | 631 | | Language | Cuda | Rust | | Adopt for | cuVS is a CUDA-based library for efficient GPU-accelerated vector search and clustering. | High-performance vector database with support for distributed deployment. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Qdrant is available under the Apache License 2.0. | | Categories | Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [cuvs](/tools/nvidia-cuvs.md) | [qdrant](/tools/qdrant-qdrant.md) | | --- | --- | --- | | Open issues (now) | 645 | 631 | | Full report | [trust report](/tools/nvidia-cuvs/trust.md) | [trust report](/tools/qdrant-qdrant/trust.md) | ## Decision facts: cuvs - **Adopt for:** cuVS is a CUDA-based library for efficient GPU-accelerated vector search and clustering. ## Decision facts: qdrant - **Hosting:** self hosted - Qdrant supports self-hosted deployment along with a cloud option at https://cloud.qdrant.io/. - **Requirements:** - Distributed deployment with sharding and replication is supported.; - No specific minimum RAM requirement provided. Performance and resource use will depend on the scale of embedding collections. - **Adopt for:** High-performance vector database with support for distributed deployment. - **License detail:** Qdrant is available under the Apache License 2.0. ## Choose when ### Choose cuvs if… - cuvs is primarily Cuda; qdrant is Rust. - Tags unique to cuvs: clustering, anns, sparse, gpu. - cuvs ships Docker support for self-hosted deployment. - - When you need high-performance vector operations leveraging the parallel processing power of GPUs, specifically with CUDA. ### Choose qdrant if… - qdrant is primarily Rust; cuvs is Cuda. - Qdrant supports self-hosted deployment along with a cloud option at https://cloud.qdrant.io/. - Requirements: - Distributed deployment with sharding and replication is supported.; - No specific minimum RAM requirement provided. Performance and resource use will depend on the scale of embedding collections.. - Tags unique to qdrant: knn-algorithm, vector-search-engine, vector-database, embeddings-similarity. - Also covers Data & Retrieval. - - When scalability and performance are paramount in handling large-scale embeddings. ## When NOT to use cuvs - - For environments where GPU resources are limited or unavailable because cuVS heavily relies on CUDA's capabilities for performance gains. - - When you prioritize portability across different hardware, as cuVS being tied to CUDA means it may not be optimal on non-NVIDIA GPUs or CPU-only systems. ## When NOT to use qdrant - - Avoid if your project requires more traditional relational database features as Qdrant focuses exclusively on vectors. - - If minimalistic setup is crucial, since Qdrant's capability for distributed deployment may introduce complexity that is not necessary for smaller-scale applications. - - For use cases where non-Rust environments significantly limit the feasibility of integrating external tools. ## Common questions ### What is the difference between cuvs and qdrant? cuvs: A library for vector search and clustering on the GPU. qdrant: High-performance, massive-scale Vector Database and Vector Search Engine. See the comparison table for live GitHub stats and shared categories. ### When should I choose cuvs over qdrant? Choose cuvs over qdrant when cuvs is primarily Cuda; qdrant is Rust; Tags unique to cuvs: clustering, anns, sparse, gpu; cuvs ships Docker support for self-hosted deployment; - When you need high-performance vector operations leveraging the parallel processing power of GPUs, specifically with CUDA. ### When should I choose qdrant over cuvs? Choose qdrant over cuvs when qdrant is primarily Rust; cuvs is Cuda; Qdrant supports self-hosted deployment along with a cloud option at https://cloud.qdrant.io/; Requirements: - Distributed deployment with sharding and replication is supported.; - No specific minimum RAM requirement provided. Performance and resource use will depend on the scale of embedding collections.; Tags unique to qdrant: knn-algorithm, vector-search-engine, vector-database, embeddings-similarity; Also covers Data & Retrieval; - When scalability and performance are paramount in handling large-scale embeddings. ### When should I avoid cuvs? - For environments where GPU resources are limited or unavailable because cuVS heavily relies on CUDA's capabilities for performance gains. - When you prioritize portability across different hardware, as cuVS being tied to CUDA means it may not be optimal on non-NVIDIA GPUs or CPU-only systems. ### When should I avoid qdrant? - Avoid if your project requires more traditional relational database features as Qdrant focuses exclusively on vectors. - If minimalistic setup is crucial, since Qdrant's capability for distributed deployment may introduce complexity that is not necessary for smaller-scale applications. - For use cases where non-Rust environments significantly limit the feasibility of integrating external tools. ### Is cuvs or qdrant more popular on GitHub? qdrant has more GitHub stars (33,143 vs 810). Stars measure visibility, not whether either tool fits your constraints. ### Are cuvs and qdrant open source? Yes - both are open-source projects on GitHub (cuvs: Apache-2.0, qdrant: Apache-2.0). ### Where can I find alternatives to cuvs or qdrant? GraphCanon lists graph-backed alternatives at [cuvs alternatives](/tools/nvidia-cuvs/alternatives) and [qdrant alternatives](/tools/qdrant-qdrant/alternatives) ([cuvs markdown twin](/tools/nvidia-cuvs/alternatives.md), [qdrant markdown twin](/tools/qdrant-qdrant/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/nvidia-cuvs-vs-qdrant-qdrant.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, cuvs or qdrant? cuvs: Very active. qdrant: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for cuvs and qdrant? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [cuvs trust report](/tools/nvidia-cuvs/trust); [qdrant trust report](/tools/qdrant-qdrant/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nvidia-cuvs`](/api/graphcanon/graph?tool=nvidia-cuvs) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_