kedro-viz vs llm-app

Common questions

What is the difference between kedro-viz and llm-app?

kedro-viz: Visualise Kedro data pipelines and track experiments.. 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 kedro-viz over llm-app?

Choose kedro-viz over llm-app when kedro-viz is primarily JavaScript; llm-app is Jupyter Notebook; License: kedro-viz is Apache-2.0, llm-app is MIT; Tags unique to kedro-viz: data-visualization, experiment tracking, kedro-extension, kedro-plugin; When working with existing Kedro projects to visualize pipelines and gain insights into experiment outcomes efficiently.

When should I choose llm-app over kedro-viz?

Choose llm-app over kedro-viz when llm-app is primarily Jupyter Notebook; kedro-viz is JavaScript; License: llm-app is MIT, kedro-viz 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, Model Training; 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 kedro-viz?

For pipeline visualization and experiment tracking if the project is not built on the Kedro framework, as integration may be cumbersome or unsupported. In settings where real-time collaboration features are essential, as its focus leans towards individual or pre-defined team workflows within a specific tech stack.

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 kedro-viz or llm-app more popular on GitHub?

llm-app has more GitHub stars (58,920 vs 761). Stars measure visibility, not whether either tool fits your constraints.

Are kedro-viz and llm-app open source?

Yes - both are open-source projects on GitHub (kedro-viz: Apache-2.0, llm-app: MIT).

Where can I find alternatives to kedro-viz or llm-app?

GraphCanon lists graph-backed alternatives at kedro-viz alternatives and llm-app alternatives (kedro-viz markdown twin, llm-app markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

Which is better maintained, kedro-viz or llm-app?

kedro-viz: 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 kedro-viz and llm-app?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: kedro-viz trust report; llm-app trust report.