--- title: "deeplake vs dingo" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/activeloopai-deeplake-vs-dingodb-dingo" tools: ["activeloopai-deeplake", "dingodb-dingo"] --- # deeplake vs dingo *GraphCanon updated Sep 30, 2026* ## Verdict Pick deeplake if deeplake is an AI Data Runtime for Agents that integrates a serverless PostgreSQL with a multimodal data lake, designed for scalable retrieval and training; pick dingo if dingoDB is a MySQL-compatible database designed for handling both structured and unstructured data with support for real-time semantic search. [deeplake](https://deeplake.ai) reports 9.2k GitHub stars, 722 forks, and 67 open issues, last pushed May 21, 2026. [dingo](https://www.dingodb.com) has 1.7k stars, 264 forks, and 8 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [deeplake's repository](https://github.com/activeloopai/deeplake) and [dingo's repository](https://github.com/dingodb/dingo). | | [deeplake](/tools/activeloopai-deeplake.md) | [dingo](/tools/dingodb-dingo.md) | | --- | --- | --- | | Tagline | AI Data Runtime for Agents | A multi-modal vector database that supports upserts and vector queries using unified SQL (MySQL-Compatible) on structured and unstructured data | | Stars | 9,239 | 1,704 | | Forks | 722 | 264 | | Open issues | 67 | 8 | | Language | C++ | Java | | Adopt for | Deeplake is an AI Data Runtime for Agents that integrates a serverless PostgreSQL with a multimodal data lake, designed for scalable retrieval and training. | DingoDB is a MySQL-compatible database designed for handling both structured and unstructured data with support for real-time semantic search. | | Persona | - | - | | Runtime | - | - | | License | Deeplake is available under the Apache-2.0 license, allowing for free use, modification, and distribution. | Apache-2.0 | | Categories | Data & Retrieval, Model Training, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deeplake](/tools/activeloopai-deeplake.md) | [dingo](/tools/dingodb-dingo.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Steady (60%) | | Days since push | 119d | 71d | | Open issues (now) | 67 | 8 | | Stars delta | +15 (30d) | +5 (30d) | | Open issues delta | +4 (30d) | 0 (30d) | | Full report | [trust report](/tools/activeloopai-deeplake/trust.md) | [trust report](/tools/dingodb-dingo/trust.md) | ## Decision facts: deeplake - **Pricing:** freemium - Deeplake offers a free tier with options for premium services, though specific pricing details are not provided in the repository. - **Requirements:** Min 4 GB RAM; Installation can be done via pip, making it accessible for Python environments. - **Adopt for:** Deeplake is an AI Data Runtime for Agents that integrates a serverless PostgreSQL with a multimodal data lake, designed for scalable retrieval and training. - **License detail:** Deeplake is available under the Apache-2.0 license, allowing for free use, modification, and distribution. ## Decision facts: dingo - **Adopt for:** DingoDB is a MySQL-compatible database designed for handling both structured and unstructured data with support for real-time semantic search. ## Choose when ### Choose deeplake if… - deeplake is primarily C++; dingo is Java. - Pricing: Deeplake offers a free tier with options for premium services, though specific pricing details are not provided in the repository.. - Requirements: Min 4 GB RAM; Installation can be done via pip, making it accessible for Python environments.. - Tags unique to deeplake: agent, agentic-rag, ai, clawbot. - Also covers Model Training. - When you need a serverless PostgreSQL setup integrated with a multimodal data lake for efficient data retrieval and training. ### Choose dingo if… - dingo is primarily Java; deeplake is C++. - Tags unique to dingo: embedding-search, embedding-store, hybrid-search, key-value-distributed-store. - You need a unified SQL interface for vector queries on diverse data types ## When NOT to use deeplake - If your project does not require serverless PostgreSQL or multimodal data handling capabilities. - When you are looking for a solution that does not support C++ as the primary language, as Deeplake is built with C++. - If your use case does not benefit from the specific integration of PostgreSQL with a data lake, and you prefer a more traditional database setup. ## When NOT to use dingo - If your application strictly demands non-SQL interfaces for querying - When the Apache-2.0 license is incompatible with your project requirements ## Common questions ### What is the difference between deeplake and dingo? deeplake: AI Data Runtime for Agents. dingo: A multi-modal vector database that supports upserts and vector queries using unified SQL (MySQL-Compatible) on structured and unstructured data. See the comparison table for live GitHub stats and shared categories. ### When should I choose deeplake over dingo? Choose deeplake over dingo when deeplake is primarily C++; dingo is Java; Pricing: Deeplake offers a free tier with options for premium services, though specific pricing details are not provided in the repository.; Requirements: Min 4 GB RAM; Installation can be done via pip, making it accessible for Python environments.; Tags unique to deeplake: agent, agentic-rag, ai, clawbot; Also covers Model Training; When you need a serverless PostgreSQL setup integrated with a multimodal data lake for efficient data retrieval and training. ### When should I choose dingo over deeplake? Choose dingo over deeplake when dingo is primarily Java; deeplake is C++; Tags unique to dingo: embedding-search, embedding-store, hybrid-search, key-value-distributed-store; You need a unified SQL interface for vector queries on diverse data types. ### When should I avoid deeplake? If your project does not require serverless PostgreSQL or multimodal data handling capabilities. When you are looking for a solution that does not support C++ as the primary language, as Deeplake is built with C++. If your use case does not benefit from the specific integration of PostgreSQL with a data lake, and you prefer a more traditional database setup. ### When should I avoid dingo? If your application strictly demands non-SQL interfaces for querying When the Apache-2.0 license is incompatible with your project requirements ### Is deeplake or dingo more popular on GitHub? deeplake has more GitHub stars (9,239 vs 1,704). Stars measure visibility, not whether either tool fits your constraints. ### Are deeplake and dingo open source? Yes - both are open-source projects on GitHub (deeplake: Apache-2.0, dingo: Apache-2.0). ### Where can I find alternatives to deeplake or dingo? GraphCanon lists graph-backed alternatives at [deeplake alternatives](/tools/activeloopai-deeplake/alternatives) and [dingo alternatives](/tools/dingodb-dingo/alternatives) ([deeplake markdown twin](/tools/activeloopai-deeplake/alternatives.md), [dingo markdown twin](/tools/dingodb-dingo/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/activeloopai-deeplake-vs-dingodb-dingo.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deeplake or dingo? deeplake: Slowing. dingo: Steady. 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 deeplake and dingo? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deeplake trust report](/tools/activeloopai-deeplake/trust); [dingo trust report](/tools/dingodb-dingo/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=activeloopai-deeplake`](/api/graphcanon/graph?tool=activeloopai-deeplake) - 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/_