--- title: "databend vs recipes" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/databendlabs-databend-vs-weaviate-recipes" tools: ["databendlabs-databend", "weaviate-recipes"] --- # databend vs recipes *GraphCanon updated Aug 21, 2026* ## Verdict Pick databend if data Agent Ready Warehouse built in Rust for analytics, search, AI, and more within a unified architecture on top of your S3 storage; pick recipes if comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases. [databend](https://docs.databend.com) reports 9.4k GitHub stars, 891 forks, and 557 open issues, last pushed Aug 21, 2026. [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 [databend's repository](https://github.com/databendlabs/databend) and [recipes's repository](https://github.com/weaviate/recipes). | | [databend](/tools/databendlabs-databend.md) | [recipes](/tools/weaviate-recipes.md) | | --- | --- | --- | | Tagline | All-in-One Data Warehouse: Analytics, Search, AI, and Python Sandboxing Reimagined From Scratch. | End-to-end notebooks for using Weaviate features and integrations. | | Stars | 9,420 | 944 | | Forks | 891 | 197 | | Open issues | 557 | 4 | | Language | Rust | Jupyter Notebook | | Adopt for | Data Agent Ready Warehouse built in Rust for analytics, search, AI, and more within a unified architecture on top of your S3 storage. | Comprehensive notebooks covering Weaviate features including vector search, media search, multi-tenancy configurations and integration use cases. | | Persona | - | - | | Runtime | - | - | | License | Other | - | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [databend](/tools/databendlabs-databend.md) | [recipes](/tools/weaviate-recipes.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 8d | | Open issues (now) | 557 | 4 | | Stars delta | +31 (30d) | +3 (30d) | | Open issues delta | +23 (30d) | -2 (30d) | | Full report | [trust report](/tools/databendlabs-databend/trust.md) | [trust report](/tools/weaviate-recipes/trust.md) | ## Decision facts: databend - **Adopt for:** Data Agent Ready Warehouse built in Rust for analytics, search, AI, and more within a unified architecture on top of your S3 storage. ## 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 databend if… - databend is primarily Rust; recipes is Jupyter Notebook. - Tags unique to databend: ai, bigdata, cloud-native, database. - - When you need a unified data platform that can handle analytics, search, and AI all from one interface, with support for vector database functions. ### Choose recipes if… - recipes is primarily Jupyter Notebook; databend is Rust. - 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 databend - - When specific integration requirements are outside of S3 support, as Databend focuses on this particular ecosystem. - - For organizations that cannot or prefer not to use technologies built in Rust due to team expertise or existing tech stack conflicts. - - If your primary need is for a solution that heavily integrates with Elasticsearch given the competitive landscape and features it offers. ## 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 databend and recipes? databend: All-in-One Data Warehouse: Analytics, Search, AI, and Python Sandboxing Reimagined From Scratch.. 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 databend over recipes? Choose databend over recipes when databend is primarily Rust; recipes is Jupyter Notebook; Tags unique to databend: ai, bigdata, cloud-native, database; - When you need a unified data platform that can handle analytics, search, and AI all from one interface, with support for vector database functions. ### When should I choose recipes over databend? Choose recipes over databend when recipes is primarily Jupyter Notebook; databend is Rust; 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 databend? - When specific integration requirements are outside of S3 support, as Databend focuses on this particular ecosystem. - For organizations that cannot or prefer not to use technologies built in Rust due to team expertise or existing tech stack conflicts. - If your primary need is for a solution that heavily integrates with Elasticsearch given the competitive landscape and features it offers. ### 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 databend or recipes more popular on GitHub? databend has more GitHub stars (9,420 vs 944). Stars measure visibility, not whether either tool fits your constraints. ### Are databend and recipes open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to databend or recipes? GraphCanon lists graph-backed alternatives at [databend alternatives](/tools/databendlabs-databend/alternatives) and [recipes alternatives](/tools/weaviate-recipes/alternatives) ([databend markdown twin](/tools/databendlabs-databend/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/databendlabs-databend-vs-weaviate-recipes.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, databend or recipes? databend: Very active. 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 databend and recipes? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [databend trust report](/tools/databendlabs-databend/trust); [recipes trust report](/tools/weaviate-recipes/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=databendlabs-databend`](/api/graphcanon/graph?tool=databendlabs-databend) - 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/_