--- title: "dingo vs qdrant" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dingodb-dingo-vs-qdrant-qdrant" tools: ["dingodb-dingo", "qdrant-qdrant"] --- # dingo vs qdrant Neutral, constraint-first comparison with live GitHub stats. | | [dingo](/tools/dingodb-dingo.md) | [qdrant](/tools/qdrant-qdrant.md) | | --- | --- | --- | | Tagline | A multi-modal vector database that supports upserts and vector queries using unified SQL on structured and unstructured data | High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. | | Stars | 1,701 | 33,026 | | Forks | 264 | 2,466 | | Open issues | 8 | 621 | | Language | Java | Rust | | 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 | 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 | Dingo is licensed under Apache-2.0 | Apache-2.0 | | Categories | Data & Retrieval, Vector Databases | Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dingo](/tools/dingodb-dingo.md) | [qdrant](/tools/qdrant-qdrant.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 43d | 0d | | Open issues (now) | 8 | 621 | | Security scan | Not scanned | No lockfile | | Full report | [trust report](/tools/dingodb-dingo/trust.md) | [trust report](/tools/qdrant-qdrant/trust.md) | **Typed relationship:** dingo _(alternative)_ qdrant Dingo and Qdrant both are vector databases supporting high-performance similarity searches, but Dingo additionally supports SQL-like query capabilities and integrates relational semantics. ## 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: 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 dingo if… - dingo is primarily Java; qdrant is Rust. - Requirements: Min 8 GB RAM; Requires Docker. - Dingo and Qdrant both are vector databases supporting high-performance similarity searches, but Dingo additionally supports SQL-like query capabilities and integrates relational semantics. - Tags unique to dingo: key-value-distributed-store, unified-sql, real-time-semantic-search, embedding-search. - Also covers Data & Retrieval. - - When you require efficient handling of mixed structured and unstructured data within an enterprise-grade solution with high availability. ### Choose qdrant if… - qdrant is primarily Rust; dingo is Java. - Dingo and Qdrant both are vector databases supporting high-performance similarity searches, but Dingo additionally supports SQL-like query capabilities and integrates relational semantics. - 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 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 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 dingo and qdrant? dingo: A multi-modal vector database that supports upserts and vector queries using unified SQL on structured and unstructured data. 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 dingo over qdrant? Choose dingo over qdrant when dingo is primarily Java; qdrant is Rust; Requirements: Min 8 GB RAM; Requires Docker; Dingo and Qdrant both are vector databases supporting high-performance similarity searches, but Dingo additionally supports SQL-like query capabilities and integrates relational semantics; Tags unique to dingo: key-value-distributed-store, unified-sql, real-time-semantic-search, embedding-search; Also covers Data & Retrieval; - When you require efficient handling of mixed structured and unstructured data within an enterprise-grade solution with high availability. ### When should I choose qdrant over dingo? Choose qdrant over dingo when qdrant is primarily Rust; dingo is Java; Dingo and Qdrant both are vector databases supporting high-performance similarity searches, but Dingo additionally supports SQL-like query capabilities and integrates relational semantics; 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 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 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 dingo or qdrant more popular on GitHub? qdrant has more GitHub stars (33,026 vs 1,701). Stars measure visibility, not whether either tool fits your constraints. ### Are dingo and qdrant open source? Yes - both are open-source projects on GitHub (dingo: Apache-2.0, qdrant: Apache-2.0). ### Where can I find alternatives to dingo or qdrant? GraphCanon lists graph-backed alternatives at /tools/dingodb-dingo/alternatives and /tools/qdrant-qdrant/alternatives (/tools/dingodb-dingo/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/dingodb-dingo-vs-qdrant-qdrant.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, dingo or qdrant? dingo: Steady. 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 dingo and qdrant? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: dingo: /tools/dingodb-dingo/trust; qdrant: /tools/qdrant-qdrant/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/_