--- title: "Daft vs llm-app" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eventual-inc-daft-vs-pathwaycom-llm-app" tools: ["eventual-inc-daft", "pathwaycom-llm-app"] --- # Daft vs llm-app *GraphCanon updated Sep 20, 2026* ## Verdict 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; pick llm-app if llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. [Daft](https://daft.ai) reports 5.8k GitHub stars, 561 forks, and 389 open issues, last pushed Sep 17, 2026. [llm-app](https://pathway.com/developers/templates/) has 59k stars, 1.5k forks, and 8 open issues, last pushed Jul 5, 2026. Figures are from public GitHub metadata via [Daft's repository](https://github.com/Eventual-Inc/Daft) and [llm-app's repository](https://github.com/pathwaycom/llm-app). | | [Daft](/tools/eventual-inc-daft.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Tagline | High-performance data engine for AI and multimodal workloads in Rust. | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data | | Stars | 5,779 | 58,920 | | Forks | 561 | 1,498 | | Open issues | 389 | 8 | | Language | Rust | Jupyter Notebook | | 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. | llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT License | | Categories | Data & Retrieval, Model Training | Data & Retrieval, Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Daft](/tools/eventual-inc-daft.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 2d | 74d | | Open issues (now) | 389 | 8 | | Stars delta | +130 (30d) | -117 (30d) | | Open issues delta | +47 (30d) | 0 (30d) | | Full report | [trust report](/tools/eventual-inc-daft/trust.md) | [trust report](/tools/pathwaycom-llm-app/trust.md) | ## 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. ## Decision facts: llm-app - **Pricing:** freemium - The repository is open-source under the MIT License, but additional services or support might incur costs. - **Requirements:** Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. - **Adopt for:** llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. - **License detail:** MIT License ## Choose when ### Choose Daft if… - Daft is primarily Rust; llm-app is Jupyter Notebook. - License: Daft is Apache-2.0, llm-app is MIT. - 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 ### Choose llm-app if… - llm-app is primarily Jupyter Notebook; Daft is Rust. - License: llm-app is MIT, Daft is Apache-2.0. - Pricing: The repository is open-source under the MIT License, but additional services or support might incur costs.. - Requirements: Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs.. - Tags unique to llm-app: chatbot, hugging-face, llm, llm-local. - Also covers Evaluation & Observability, Inference & Serving. - When you need ready-to-run cloud templates for RAG, AI pipelines, and enterprise search that integrate seamlessly with data sources such as Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-ti ## 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 ## When NOT to use llm-app - Avoid using llm-app if your project does not require integration with specific data sources like Sharepoint or Google Drive, as the tool's strength lies in its broad data source support. - Do not use llm-app if you are looking for a tool that focuses solely on model training or inference without the need for cloud templates or enterprise search capabilities. ## Common questions ### What is the difference between Daft and llm-app? Daft: High-performance data engine for AI and multimodal workloads in Rust.. llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. See the comparison table for live GitHub stats and shared categories. ### When should I choose Daft over llm-app? Choose Daft over llm-app when Daft is primarily Rust; llm-app is Jupyter Notebook; License: Daft is Apache-2.0, llm-app is MIT; 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 choose llm-app over Daft? Choose llm-app over Daft when llm-app is primarily Jupyter Notebook; Daft is Rust; License: llm-app is MIT, Daft is Apache-2.0; Pricing: The repository is open-source under the MIT License, but additional services or support might incur costs.; Requirements: Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs.; Tags unique to llm-app: chatbot, hugging-face, llm, llm-local; Also covers Evaluation & Observability, Inference & Serving; When you need ready-to-run cloud templates for RAG, AI pipelines, and enterprise search that integrate seamlessly with data sources such as Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-ti. ### 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 ### When should I avoid llm-app? Avoid using llm-app if your project does not require integration with specific data sources like Sharepoint or Google Drive, as the tool's strength lies in its broad data source support. Do not use llm-app if you are looking for a tool that focuses solely on model training or inference without the need for cloud templates or enterprise search capabilities. ### Is Daft or llm-app more popular on GitHub? llm-app has more GitHub stars (58,920 vs 5,779). Stars measure visibility, not whether either tool fits your constraints. ### Are Daft and llm-app open source? Yes - both are open-source projects on GitHub (Daft: Apache-2.0, llm-app: MIT). ### Where can I find alternatives to Daft or llm-app? GraphCanon lists graph-backed alternatives at [Daft alternatives](/tools/eventual-inc-daft/alternatives) and [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) ([Daft markdown twin](/tools/eventual-inc-daft/alternatives.md), [llm-app markdown twin](/tools/pathwaycom-llm-app/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/eventual-inc-daft-vs-pathwaycom-llm-app.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Daft or llm-app? Daft: Very active. llm-app: 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 Daft and llm-app? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Daft trust report](/tools/eventual-inc-daft/trust); [llm-app trust report](/tools/pathwaycom-llm-app/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=eventual-inc-daft`](/api/graphcanon/graph?tool=eventual-inc-daft) - 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/_