--- title: "dingo vs lancedb" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dingodb-dingo-vs-lancedb-lancedb" tools: ["dingodb-dingo", "lancedb-lancedb"] --- # dingo vs lancedb Neutral, constraint-first comparison with live GitHub stats. | | [dingo](/tools/dingodb-dingo.md) | [lancedb](/tools/lancedb-lancedb.md) | | --- | --- | --- | | Tagline | A multi-modal vector database that supports upserts and vector queries using unified SQL on structured and unstructured data | Developer-friendly OSS embedded retrieval library for multimodal AI | | Stars | 1,701 | 10,825 | | Forks | 264 | 939 | | Open issues | 8 | 640 | | Language | Java | HTML | | Adopt for | Dingo is a multi-modal vector database adept at serving both structured and unstructured data, featuring MySQL compatibility, high concurrency, low latency, and real-time scalar-vector hybrid retrieval. It supports SQL,悠 | LanceDB is an open-source embedded retrieval library optimized for multimodal AI applications. It supports vector search, full-text queries and SQL through various interfaces including Python, Rust, Node.js, and REST API | | Persona | - | - | | Runtime | - | - | | License | Dingo is licensed under Apache-2.0 | LanceDB is distributed under the Apache-2.0 license, allowing for broad usage with attribution requirements and no patent grants | | Categories | Data & Retrieval, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dingo](/tools/dingodb-dingo.md) | [lancedb](/tools/lancedb-lancedb.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 43d | 0d | | Open issues (now) | 8 | 640 | | Full report | [trust report](/tools/dingodb-dingo/trust.md) | [trust report](/tools/lancedb-lancedb/trust.md) | **Typed relationship:** dingo _(alternative)_ lancedb Both DingoDB and LanceDB serve as multi-modal vector databases, offering solutions for embedding storage and search. They compete by providing similar functionalities but may approach the problem differently. ## Decision facts: dingo - **Requirements:** Min 8 GB RAM; Requires Docker - **Adopt for:** Dingo is a multi-modal vector database adept at serving both structured and unstructured data, featuring MySQL compatibility, high concurrency, low latency, and real-time scalar-vector hybrid retrieval. It supports SQL,悠 - **License detail:** Dingo is licensed under Apache-2.0 ## Decision facts: lancedb - **Pricing:** freemium - Open-source local version available for free; cloud services likely come at a cost due to its capability of handling production-scale workloads without server management - **Requirements:** - Requires relevant SDKs for programming languages Python, Typescript/Node.js, or Rust according to integration needs; - GPU support for vector indexing can further improve performance, but it is not mandatory for basic operations - **Adopt for:** LanceDB is an open-source embedded retrieval library optimized for multimodal AI applications. It supports vector search, full-text queries and SQL through various interfaces including Python, Rust, Node.js, and REST API - **License detail:** LanceDB is distributed under the Apache-2.0 license, allowing for broad usage with attribution requirements and no patent grants ## Choose when ### Choose dingo if… - dingo is primarily Java; lancedb is HTML. - Requirements: Min 8 GB RAM; Requires Docker. - Both DingoDB and LanceDB serve as multi-modal vector databases, offering solutions for embedding storage and search. They compete by providing similar functionalities but may approach the problem differently. - Tags unique to dingo: key-value-distributed-store, unified-sql, real-time-semantic-search, embedding-search. - - When you require efficient handling of mixed structured and unstructured data within an enterprise-grade solution with high availability. ### Choose lancedb if… - lancedb is primarily HTML; dingo is Java. - Pricing: Open-source local version available for free; cloud services likely come at a cost due to its capability of handling production-scale workloads without server management. - Requirements: - Requires relevant SDKs for programming languages Python, Typescript/Node.js, or Rust according to integration needs; - GPU support for vector indexing can further improve performance, but it is not mandatory for basic operations. - Both DingoDB and LanceDB serve as multi-modal vector databases, offering solutions for embedding storage and search. They compete by providing similar functionalities but may approach the problem differently. - Tags unique to lancedb: similarity-search, vector-database, semantic-search, search-engine. - lancedb ships Docker support for self-hosted deployment. - - You require efficient handling of large volumes of multimodal data (text, images, video) across different query forms ## When NOT to use dingo - - If you are looking for a solution that does not offer MySQL compatibility or SQL support for vector databases. - - When your application’s requirements do not include hybrid scalar-vector retrievals and real-time index optimization capabilities. - - If your project specifically excludes Java-based solutions or does not benefit from the automatic elastic sharding Dingo provides. ## When NOT to use lancedb - - You prefer lightweight or simple setups where the overhead of managing versions and advanced indexing capabilities provided by LanceDB is unnecessary - - Projects are strictly confined to single-modal data that does not require complex vector search operations or do not benefit from SQL or full-text querying features offered by LanceDB ## Common questions ### What is the difference between dingo and lancedb? dingo: A multi-modal vector database that supports upserts and vector queries using unified SQL on structured and unstructured data. lancedb: Developer-friendly OSS embedded retrieval library for multimodal AI. See the comparison table for live GitHub stats and shared categories. ### When should I choose dingo over lancedb? Choose dingo over lancedb when dingo is primarily Java; lancedb is HTML; Requirements: Min 8 GB RAM; Requires Docker; Both DingoDB and LanceDB serve as multi-modal vector databases, offering solutions for embedding storage and search. They compete by providing similar functionalities but may approach the problem differently; Tags unique to dingo: key-value-distributed-store, unified-sql, real-time-semantic-search, embedding-search; - When you require efficient handling of mixed structured and unstructured data within an enterprise-grade solution with high availability. ### When should I choose lancedb over dingo? Choose lancedb over dingo when lancedb is primarily HTML; dingo is Java; Pricing: Open-source local version available for free; cloud services likely come at a cost due to its capability of handling production-scale workloads without server management; Requirements: - Requires relevant SDKs for programming languages Python, Typescript/Node.js, or Rust according to integration needs; - GPU support for vector indexing can further improve performance, but it is not mandatory for basic operations; Both DingoDB and LanceDB serve as multi-modal vector databases, offering solutions for embedding storage and search. They compete by providing similar functionalities but may approach the problem differently; Tags unique to lancedb: similarity-search, vector-database, semantic-search, search-engine; lancedb ships Docker support for self-hosted deployment; - You require efficient handling of large volumes of multimodal data (text, images, video) across different query forms. ### When should I avoid dingo? - If you are looking for a solution that does not offer MySQL compatibility or SQL support for vector databases. - When your application’s requirements do not include hybrid scalar-vector retrievals and real-time index optimization capabilities. - If your project specifically excludes Java-based solutions or does not benefit from the automatic elastic sharding Dingo provides. ### When should I avoid lancedb? - You prefer lightweight or simple setups where the overhead of managing versions and advanced indexing capabilities provided by LanceDB is unnecessary - Projects are strictly confined to single-modal data that does not require complex vector search operations or do not benefit from SQL or full-text querying features offered by LanceDB ### Is dingo or lancedb more popular on GitHub? lancedb has more GitHub stars (10,825 vs 1,701). Stars measure visibility, not whether either tool fits your constraints. ### Are dingo and lancedb open source? Yes - both are open-source projects on GitHub (dingo: Apache-2.0, lancedb: Apache-2.0). ### Where can I find alternatives to dingo or lancedb? GraphCanon lists graph-backed alternatives at /tools/dingodb-dingo/alternatives and /tools/lancedb-lancedb/alternatives (/tools/dingodb-dingo/alternatives.md, /tools/lancedb-lancedb/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/dingodb-dingo-vs-lancedb-lancedb.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, dingo or lancedb? dingo: Steady. lancedb: 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 dingo and lancedb? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: dingo: /tools/dingodb-dingo/trust; lancedb: /tools/lancedb-lancedb/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dingodb-dingo`](/api/graphcanon/graph?tool=dingodb-dingo) - 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/_