--- title: "cuvs vs examples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvidia-cuvs-vs-pinecone-io-examples" tools: ["nvidia-cuvs", "pinecone-io-examples"] --- # cuvs vs examples *GraphCanon updated Jul 12, 2026* ## Verdict Pick cuvs if cuVS is a CUDA-based library for efficient GPU-accelerated vector search and clustering; pick examples if examples, powered by Pinecone vector databases, offers interactive Jupyter Notebooks to aid users in experimenting with semantic search tasks through hands-on guidance. [cuvs](https://docs.rapids.ai/api/cuvs/stable/) reports 810 GitHub stars, 210 forks, and 645 open issues, last pushed Jul 11, 2026. [examples](https://github.com/pinecone-io/examples) has 3.0k stars, 1.1k forks, and 63 open issues, last pushed Jul 9, 2026. Figures are from public GitHub metadata via [cuvs's repository](https://github.com/NVIDIA/cuvs) and [examples's repository](https://github.com/pinecone-io/examples). | | [cuvs](/tools/nvidia-cuvs.md) | [examples](/tools/pinecone-io-examples.md) | | --- | --- | --- | | Tagline | A library for vector search and clustering on the GPU | Jupyter Notebooks to help you get hands-on with Pinecone vector databases | | Stars | 810 | 3,026 | | Forks | 210 | 1,072 | | Open issues | 645 | 63 | | Language | Cuda | Jupyter Notebook | | Adopt for | cuVS is a CUDA-based library for efficient GPU-accelerated vector search and clustering. | Examples, powered by Pinecone vector databases, offers interactive Jupyter Notebooks to aid users in experimenting with semantic search tasks through hands-on guidance. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Vector Databases | Vector Databases, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [cuvs](/tools/nvidia-cuvs.md) | [examples](/tools/pinecone-io-examples.md) | | --- | --- | --- | | Days since push | 0d | 2d | | Open issues (now) | 645 | 63 | | Full report | [trust report](/tools/nvidia-cuvs/trust.md) | [trust report](/tools/pinecone-io-examples/trust.md) | ## Decision facts: cuvs - **Adopt for:** cuVS is a CUDA-based library for efficient GPU-accelerated vector search and clustering. ## Decision facts: examples - **Adopt for:** Examples, powered by Pinecone vector databases, offers interactive Jupyter Notebooks to aid users in experimenting with semantic search tasks through hands-on guidance. ## Choose when ### Choose cuvs if… - cuvs is primarily Cuda; examples is Jupyter Notebook. - License: cuvs is Apache-2.0, examples is MIT. - 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 examples if… - examples is primarily Jupyter Notebook; cuvs is Cuda. - License: examples is MIT, cuvs is Apache-2.0. - Tags unique to examples: vector-database, llm, ai, python. - Also covers Data & Retrieval. - When you need specific examples and walkthroughs for working with Pinecone's vector database technology using interactive Jupyter Notebooks. ## 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 examples - Avoid if you're looking for generic tools applicable to a wide range of vector databases; this repository focuses exclusively on Pinecone. - Not ideal if you prefer starting with theoretical understanding before practical application; the provided guidance is geared toward immediate experimentation in Google Colab. ## Common questions ### What is the difference between cuvs and examples? cuvs: A library for vector search and clustering on the GPU. examples: Jupyter Notebooks to help you get hands-on with Pinecone vector databases. See the comparison table for live GitHub stats and shared categories. ### When should I choose cuvs over examples? Choose cuvs over examples when cuvs is primarily Cuda; examples is Jupyter Notebook; License: cuvs is Apache-2.0, examples is MIT; 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 examples over cuvs? Choose examples over cuvs when examples is primarily Jupyter Notebook; cuvs is Cuda; License: examples is MIT, cuvs is Apache-2.0; Tags unique to examples: vector-database, llm, ai, python; Also covers Data & Retrieval; When you need specific examples and walkthroughs for working with Pinecone's vector database technology using interactive Jupyter Notebooks. ### 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 examples? Avoid if you're looking for generic tools applicable to a wide range of vector databases; this repository focuses exclusively on Pinecone. Not ideal if you prefer starting with theoretical understanding before practical application; the provided guidance is geared toward immediate experimentation in Google Colab. ### Is cuvs or examples more popular on GitHub? examples has more GitHub stars (3,026 vs 810). Stars measure visibility, not whether either tool fits your constraints. ### Are cuvs and examples open source? Yes - both are open-source projects on GitHub (cuvs: Apache-2.0, examples: MIT). ### Where can I find alternatives to cuvs or examples? GraphCanon lists graph-backed alternatives at [cuvs alternatives](/tools/nvidia-cuvs/alternatives) and [examples alternatives](/tools/pinecone-io-examples/alternatives) ([cuvs markdown twin](/tools/nvidia-cuvs/alternatives.md), [examples markdown twin](/tools/pinecone-io-examples/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-pinecone-io-examples.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, cuvs or examples? cuvs: Very active. examples: 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 examples? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [cuvs trust report](/tools/nvidia-cuvs/trust); [examples trust report](/tools/pinecone-io-examples/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/_