--- title: "aquila vs recipes" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aquila-network-aquila-vs-weaviate-recipes" tools: ["aquila-network-aquila", "weaviate-recipes"] --- # aquila vs recipes *GraphCanon updated Aug 21, 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 recipes if comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases. [aquila](https://aquila.network) reports 379 GitHub stars, 26 forks, and 13 open issues, last pushed May 6, 2024. [recipes](https://github.com/weaviate/recipes) has 944 stars, 197 forks, and 4 open issues, last pushed Aug 13, 2026. Figures are from public GitHub metadata via [aquila's repository](https://github.com/Aquila-Network/aquila) and [recipes's repository](https://github.com/weaviate/recipes). | | [aquila](/tools/aquila-network-aquila.md) | [recipes](/tools/weaviate-recipes.md) | | --- | --- | --- | | Tagline | Efficient Neural Search Engine | End-to-end notebooks for using Weaviate features and integrations. | | Stars | 379 | 944 | | Forks | 26 | 197 | | Open issues | 13 | 4 | | 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. | Comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases. | | Persona | - | - | | Runtime | - | - | | License | - | - | | 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) | [recipes](/tools/weaviate-recipes.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 817d | 8d | | Open issues (now) | 13 | 4 | | Stars delta | Unknown | +3 (30d) | | Open issues delta | Unknown | -2 (30d) | | Full report | [trust report](/tools/aquila-network-aquila/trust.md) | [trust report](/tools/weaviate-recipes/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: recipes - **Adopt for:** Comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases. ## Choose when ### Choose aquila if… - aquila is primarily HTML; recipes 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 recipes if… - recipes is primarily Jupyter Notebook; aquila is HTML. - Tags unique to recipes: function-calling, generative-ai, llm frameworks, python. - When you are specifically interested in exploring various integrations with cloud hyperscalers (Google, AWS), LLM frameworks (LangChain, LlamaIndex), and other technologies mentioned, such as Databri ## 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 recipes - If you are looking for generalized vector database use case examples that do not specifically showcase Weaviate's unique integrations or features - When your focus is on understanding and using broad category services instead of the specific, detailed examples and configurations available in the Weaviate ecosystem - For cases where a competitor tool offers better support for other specific needs, such as more comprehensive integration with data platforms not specifically covered by Weaviate like MongoDB or Redis ## Common questions ### What is the difference between aquila and recipes? aquila: Efficient Neural Search Engine. recipes: End-to-end notebooks for using Weaviate features and integrations.. See the comparison table for live GitHub stats and shared categories. ### When should I choose aquila over recipes? Choose aquila over recipes when aquila is primarily HTML; recipes 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 recipes over aquila? Choose recipes over aquila when recipes is primarily Jupyter Notebook; aquila is HTML; Tags unique to recipes: function-calling, generative-ai, llm frameworks, python; When you are specifically interested in exploring various integrations with cloud hyperscalers (Google, AWS), LLM frameworks (LangChain, LlamaIndex), and other technologies mentioned, such as Databri. ### 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 recipes? If you are looking for generalized vector database use case examples that do not specifically showcase Weaviate's unique integrations or features When your focus is on understanding and using broad category services instead of the specific, detailed examples and configurations available in the Weaviate ecosystem For cases where a competitor tool offers better support for other specific needs, such as more comprehensive integration with data platforms not specifically covered by Weaviate like MongoDB or Redis ### Is aquila or recipes more popular on GitHub? recipes has more GitHub stars (944 vs 379). Stars measure visibility, not whether either tool fits your constraints. ### Are aquila and recipes open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to aquila or recipes? GraphCanon lists graph-backed alternatives at [aquila alternatives](/tools/aquila-network-aquila/alternatives) and [recipes alternatives](/tools/weaviate-recipes/alternatives) ([aquila markdown twin](/tools/aquila-network-aquila/alternatives.md), [recipes markdown twin](/tools/weaviate-recipes/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-weaviate-recipes.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aquila or recipes? aquila: Dormant. recipes: 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 recipes? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aquila trust report](/tools/aquila-network-aquila/trust); [recipes trust report](/tools/weaviate-recipes/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/_