--- title: "aquila vs examples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aquila-network-aquila-vs-pinecone-io-examples" tools: ["aquila-network-aquila", "pinecone-io-examples"] --- # aquila vs examples *GraphCanon updated Aug 15, 2026* ## Verdict Pick aquila if aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches; 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. [aquila](https://aquila.network) reports 379 GitHub stars, 26 forks, and 13 open issues, last pushed May 6, 2024. [examples](https://docs.pinecone.io) has 3.0k stars, 1.1k forks, and 61 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [aquila's repository](https://github.com/Aquila-Network/aquila) and [examples's repository](https://github.com/pinecone-io/examples). | | [aquila](/tools/aquila-network-aquila.md) | [examples](/tools/pinecone-io-examples.md) | | --- | --- | --- | | Tagline | Efficient Neural Search Engine | Jupyter Notebooks to help you get hands-on with Pinecone vector databases | | Stars | 379 | 3,036 | | Forks | 26 | 1,073 | | Open issues | 13 | 61 | | Language | HTML | Jupyter Notebook | | Adopt for | Aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches. | 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 | - | MIT | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aquila](/tools/aquila-network-aquila.md) | [examples](/tools/pinecone-io-examples.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 817d | 0d | | Open issues (now) | 13 | 61 | | Stars delta | Unknown | +8 (30d) | | Open issues delta | Unknown | -3 (30d) | | Full report | [trust report](/tools/aquila-network-aquila/trust.md) | [trust report](/tools/pinecone-io-examples/trust.md) | ## Decision facts: aquila - **Adopt for:** Aquila is an efficient neural search engine designed for indexing latent vectors and JSON metadata while performing k-NN searches. ## 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 aquila if… - aquila is primarily HTML; examples is Jupyter Notebook. - Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors. - When deploying a solution that requires the processing of feature vectors in image or video search contexts, where efficiency in approximate nearest neighbor search is necessary ### Choose examples if… - examples is primarily Jupyter Notebook; aquila is HTML. - Tags unique to examples: ai, jupyter-notebook, llm, python. - When you need specific examples and walkthroughs for working with Pinecone's vector database technology using interactive Jupyter Notebooks. ## When NOT to use aquila - If the development team lacks experience with Docker, as Aquila's setup heavily relies on Docker images to run locally or in a big data configuration - In scenarios where strict control over metadata and vector indexing is required beyond what JSON and latent vectors can provide ## 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 aquila and examples? aquila: Efficient Neural Search Engine. 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 aquila over examples? Choose aquila over examples when aquila is primarily HTML; examples is Jupyter Notebook; Tags unique to aquila: approximate-nearest-neighbor-search, embedding, faiss, feature-vectors; When deploying a solution that requires the processing of feature vectors in image or video search contexts, where efficiency in approximate nearest neighbor search is necessary. ### When should I choose examples over aquila? Choose examples over aquila when examples is primarily Jupyter Notebook; aquila is HTML; Tags unique to examples: ai, jupyter-notebook, llm, python; When you need specific examples and walkthroughs for working with Pinecone's vector database technology using interactive Jupyter Notebooks. ### When should I avoid aquila? If the development team lacks experience with Docker, as Aquila's setup heavily relies on Docker images to run locally or in a big data configuration In scenarios where strict control over metadata and vector indexing is required beyond what JSON and latent vectors can provide ### 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 aquila or examples more popular on GitHub? examples has more GitHub stars (3,036 vs 379). Stars measure visibility, not whether either tool fits your constraints. ### Are aquila and examples open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to aquila or examples? GraphCanon lists graph-backed alternatives at [aquila alternatives](/tools/aquila-network-aquila/alternatives) and [examples alternatives](/tools/pinecone-io-examples/alternatives) ([aquila markdown twin](/tools/aquila-network-aquila/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/aquila-network-aquila-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, aquila or examples? aquila: Dormant. 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 aquila and examples? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aquila trust report](/tools/aquila-network-aquila/trust); [examples trust report](/tools/pinecone-io-examples/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aquila-network-aquila`](/api/graphcanon/graph?tool=aquila-network-aquila) - 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/_