--- title: "examples vs qdrant" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/pinecone-io-examples-vs-qdrant-qdrant" tools: ["pinecone-io-examples", "qdrant-qdrant"] --- # examples vs qdrant Neutral, constraint-first comparison with live GitHub stats. | | [examples](/tools/pinecone-io-examples.md) | [qdrant](/tools/qdrant-qdrant.md) | | --- | --- | --- | | Tagline | Jupyter Notebooks to help you get hands-on with Pinecone vector databases | High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. | | Stars | 3,025 | 33,026 | | Forks | 1,073 | 2,466 | | Open issues | 63 | 621 | | Language | Jupyter Notebook | Rust | | Adopt for | Examples from the Pinecone repository are tailored for hands-on learning and development with Pinecone's vector databases, featuring production-ready samples and educational materials. | Qdrant is a high-performance, massive-scale vector database and search engine that leverages Rust for its performance under heavy loads. It supports extended filtering capabilities which make it suitable for neural-net,语 | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Vector Databases | Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [examples](/tools/pinecone-io-examples.md) | [qdrant](/tools/qdrant-qdrant.md) | | --- | --- | --- | | Days since push | 5d | 0d | | Open issues (now) | 63 | 621 | | Security scan | Not scanned | No lockfile | | Full report | [trust report](/tools/pinecone-io-examples/trust.md) | [trust report](/tools/qdrant-qdrant/trust.md) | **Typed relationship:** examples _(alternative)_ qdrant Qdrant is another high-performance vector database that competes with Pinecone in the field of efficient similarity search for large-scale vector datasets. ## Decision facts: examples - **Adopt for:** Examples from the Pinecone repository are tailored for hands-on learning and development with Pinecone's vector databases, featuring production-ready samples and educational materials. ## Decision facts: qdrant - **Adopt for:** Qdrant is a high-performance, massive-scale vector database and search engine that leverages Rust for its performance under heavy loads. It supports extended filtering capabilities which make it suitable for neural-net,语 ## Choose when ### Choose examples if… - examples is primarily Jupyter Notebook; qdrant is Rust. - License: examples is MIT, qdrant is Apache-2.0. - Qdrant is another high-performance vector database that competes with Pinecone in the field of efficient similarity search for large-scale vector datasets. - Tags unique to examples: vector-database, llm, ai, python. - - You're working exclusively with the Pinecone vector database ecosystem. ### Choose qdrant if… - qdrant is primarily Rust; examples is Jupyter Notebook. - License: qdrant is Apache-2.0, examples is MIT. - Qdrant is another high-performance vector database that competes with Pinecone in the field of efficient similarity search for large-scale vector datasets. - Tags unique to qdrant: knn-algorithm, embeddings-similarity, machine-learning, ai-search. - qdrant ships Docker support for self-hosted deployment. - When you need high performance and reliability under heavy load due to Qdrant's Rust-based implementation. ## When NOT to use examples - - The primary technology focus of your project is not on vector databases powered by Pinecone. - - You seek a more generalized approach to learning about various vector database systems, as this repository is dedicated specifically to Pinecone's implementation. ## When NOT to use qdrant - Avoid using Qdrant when the primary requirement is to interact with traditional relational databases rather than vector embeddings. - Do not choose Qdrant if your project does not require or benefit from faceted search capabilities, extended filtering support, or next-generation AI functionalities. - If you prefer open-source solutions with community-driven development and less reliance on managed cloud services. ## Common questions ### What is the difference between examples and qdrant? examples: Jupyter Notebooks to help you get hands-on with Pinecone vector databases. qdrant: High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI.. See the comparison table for live GitHub stats and shared categories. ### When should I choose examples over qdrant? Choose examples over qdrant when examples is primarily Jupyter Notebook; qdrant is Rust; License: examples is MIT, qdrant is Apache-2.0; Qdrant is another high-performance vector database that competes with Pinecone in the field of efficient similarity search for large-scale vector datasets; Tags unique to examples: vector-database, llm, ai, python; - You're working exclusively with the Pinecone vector database ecosystem. ### When should I choose qdrant over examples? Choose qdrant over examples when qdrant is primarily Rust; examples is Jupyter Notebook; License: qdrant is Apache-2.0, examples is MIT; Qdrant is another high-performance vector database that competes with Pinecone in the field of efficient similarity search for large-scale vector datasets; Tags unique to qdrant: knn-algorithm, embeddings-similarity, machine-learning, ai-search; qdrant ships Docker support for self-hosted deployment; When you need high performance and reliability under heavy load due to Qdrant's Rust-based implementation. ### When should I avoid examples? - The primary technology focus of your project is not on vector databases powered by Pinecone. - You seek a more generalized approach to learning about various vector database systems, as this repository is dedicated specifically to Pinecone's implementation. ### When should I avoid qdrant? Avoid using Qdrant when the primary requirement is to interact with traditional relational databases rather than vector embeddings. Do not choose Qdrant if your project does not require or benefit from faceted search capabilities, extended filtering support, or next-generation AI functionalities. If you prefer open-source solutions with community-driven development and less reliance on managed cloud services. ### Is examples or qdrant more popular on GitHub? qdrant has more GitHub stars (33,026 vs 3,025). Stars measure visibility, not whether either tool fits your constraints. ### Are examples and qdrant open source? Yes - both are open-source projects on GitHub (examples: MIT, qdrant: Apache-2.0). ### Where can I find alternatives to examples or qdrant? GraphCanon lists graph-backed alternatives at /tools/pinecone-io-examples/alternatives and /tools/qdrant-qdrant/alternatives (/tools/pinecone-io-examples/alternatives.md, /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 /compare/pinecone-io-examples-vs-qdrant-qdrant.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, examples or qdrant? examples: 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 examples and qdrant? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: examples: /tools/pinecone-io-examples/trust; qdrant: /tools/qdrant-qdrant/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=pinecone-io-examples`](/api/graphcanon/graph?tool=pinecone-io-examples) - 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/_