--- title: "deeplake vs Daft" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/activeloopai-deeplake-vs-eventual-inc-daft" tools: ["activeloopai-deeplake", "eventual-inc-daft"] --- # deeplake vs Daft *GraphCanon updated Aug 22, 2026* ## Verdict Pick deeplake if deeplake is an AI Data Runtime for Agents designed with serverless Postgres and multimodal data lake support, targeting scalable retrieval and training capabilities; pick Daft if daft is a Rust-based high-performance data engine for AI and multimodal workloads that supports processing various types of structured and unstructured data at scale. [deeplake](https://deeplake.ai) reports 9.2k GitHub stars, 721 forks, and 63 open issues, last pushed May 21, 2026. [Daft](https://daft.ai) has 5.7k stars, 544 forks, and 371 open issues, last pushed Aug 21, 2026. Figures are from public GitHub metadata via [deeplake's repository](https://github.com/activeloopai/deeplake) and [Daft's repository](https://github.com/Eventual-Inc/Daft). | | [deeplake](/tools/activeloopai-deeplake.md) | [Daft](/tools/eventual-inc-daft.md) | | --- | --- | --- | | Tagline | AI Data Runtime for Agents with scalable retrieval and training features | High-performance data engine for AI and multimodal workloads in Rust. | | Stars | 9,224 | 5,725 | | Forks | 721 | 544 | | Open issues | 63 | 371 | | Language | C++ | Rust | | Adopt for | Deeplake is an AI Data Runtime for Agents designed with serverless Postgres and multimodal data lake support, targeting scalable retrieval and training capabilities. | Daft is a Rust-based high-performance data engine for AI and multimodal workloads that supports processing various types of structured and unstructured data at scale. | | Persona | - | - | | Runtime | - | - | | License | Deeplake uses the Apache-2.0 license, allowing free use in both open source and commercial projects with attribution. | Apache-2.0 | | Categories | Data & Retrieval, Model Training, Vector Databases | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deeplake](/tools/activeloopai-deeplake.md) | [Daft](/tools/eventual-inc-daft.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 87d | 0d | | Open issues (now) | 63 | 371 | | Stars delta | +16 (30d) | +76 (30d) | | Open issues delta | -6 (30d) | +29 (30d) | | Full report | [trust report](/tools/activeloopai-deeplake/trust.md) | [trust report](/tools/eventual-inc-daft/trust.md) | ## Shared compatibility - **Python**: [deeplake](/tools/activeloopai-deeplake.md) - Python runtime; [Daft](/tools/eventual-inc-daft.md) - Python runtime ## Decision facts: deeplake - **Pricing:** unknown - Pricing details are not specified for Deeplake's public repository. - **Requirements:** Deeplake can be installed using pip, making it accessible via the command `pip install deeplake`. - **Adopt for:** Deeplake is an AI Data Runtime for Agents designed with serverless Postgres and multimodal data lake support, targeting scalable retrieval and training capabilities. - **License detail:** Deeplake uses the Apache-2.0 license, allowing free use in both open source and commercial projects with attribution. ## Decision facts: Daft - **Adopt for:** Daft is a Rust-based high-performance data engine for AI and multimodal workloads that supports processing various types of structured and unstructured data at scale. ## Choose when ### Choose deeplake if… - deeplake is primarily C++; Daft is Rust. - Pricing: Pricing details are not specified for Deeplake's public repository.. - Requirements: Deeplake can be installed using pip, making it accessible via the command `pip install deeplake`.. - Tags unique to deeplake: agent, agentic-rag, ai, computer-vision. - Also covers Vector Databases. - When you are developing applications that require seamless integration with AI agents, as Deeplake supports agent-centric design. ### Choose Daft if… - Daft is primarily Rust; deeplake is C++. - Tags unique to Daft: ai-engineering, ai-pipeline, arrow, artificial-intelligence. - - When you require high performance and efficiency in a multilingual environment, particularly if projects are primarily developed in Rust ## When NOT to use deeplake - If your project does not benefit from an agent-centric architecture and you primarily require traditional database operations without multimodal features. - When cost control is critical and serverless PostgreSQL might introduce variable costs compared to on-premises solutions for data retrieval and training. ## When NOT to use Daft - - Avoid using Daft for projects where Python dominates the tech stack or development ecosystem - - When performance requirements are lower and ease of use is prioritized over speed ## Common questions ### What is the difference between deeplake and Daft? deeplake: AI Data Runtime for Agents with scalable retrieval and training features. Daft: High-performance data engine for AI and multimodal workloads in Rust.. See the comparison table for live GitHub stats and shared categories. ### When should I choose deeplake over Daft? Choose deeplake over Daft when deeplake is primarily C++; Daft is Rust; Pricing: Pricing details are not specified for Deeplake's public repository.; Requirements: Deeplake can be installed using pip, making it accessible via the command `pip install deeplake`.; Tags unique to deeplake: agent, agentic-rag, ai, computer-vision; Also covers Vector Databases; When you are developing applications that require seamless integration with AI agents, as Deeplake supports agent-centric design. ### When should I choose Daft over deeplake? Choose Daft over deeplake when Daft is primarily Rust; deeplake is C++; Tags unique to Daft: ai-engineering, ai-pipeline, arrow, artificial-intelligence; - When you require high performance and efficiency in a multilingual environment, particularly if projects are primarily developed in Rust. ### When should I avoid deeplake? If your project does not benefit from an agent-centric architecture and you primarily require traditional database operations without multimodal features. When cost control is critical and serverless PostgreSQL might introduce variable costs compared to on-premises solutions for data retrieval and training. ### When should I avoid Daft? - Avoid using Daft for projects where Python dominates the tech stack or development ecosystem - When performance requirements are lower and ease of use is prioritized over speed ### Is deeplake or Daft more popular on GitHub? deeplake has more GitHub stars (9,224 vs 5,725). Stars measure visibility, not whether either tool fits your constraints. ### Are deeplake and Daft open source? Yes - both are open-source projects on GitHub (deeplake: Apache-2.0, Daft: Apache-2.0). ### Where can I find alternatives to deeplake or Daft? GraphCanon lists graph-backed alternatives at [deeplake alternatives](/tools/activeloopai-deeplake/alternatives) and [Daft alternatives](/tools/eventual-inc-daft/alternatives) ([deeplake markdown twin](/tools/activeloopai-deeplake/alternatives.md), [Daft markdown twin](/tools/eventual-inc-daft/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-eventual-inc-daft.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deeplake or Daft? deeplake: Steady. Daft: 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 deeplake and Daft? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deeplake trust report](/tools/activeloopai-deeplake/trust); [Daft trust report](/tools/eventual-inc-daft/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/_