--- title: "llm-course vs onWatch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mlabonne-llm-course-vs-onllm-dev-onwatch" tools: ["mlabonne-llm-course", "onllm-dev-onwatch"] --- # llm-course vs onWatch *GraphCanon updated Jul 11, 2026* ## Verdict Pick llm-course when license: llm-course is Apache-2.0, onWatch is GPL-3.0; pick onWatch when license: onWatch is GPL-3.0, llm-course is Apache-2.0. [llm-course](https://mlabonne.github.io/blog/) reports 81k GitHub stars, 9.4k forks, and 84 open issues, last pushed Feb 5, 2026. [onWatch](https://onwatch.onllm.dev) has 673 stars, 51 forks, and 11 open issues, last pushed Jun 19, 2026. Figures are from public GitHub metadata via [llm-course's repository](https://github.com/mlabonne/llm-course) and [onWatch's repository](https://github.com/onllm-dev/onWatch). | | [llm-course](/tools/mlabonne-llm-course.md) | [onWatch](/tools/onllm-dev-onwatch.md) | | --- | --- | --- | | Tagline | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | Track AI API quotas across Synthetic, Z.ai, Anthropic (Claude Code), Codex, GitHub Copilot & Antigravity in real time. Lightweight background daemon (<50MB RAM), SQLite storage, Material Design 3 dash | | Stars | 80,839 | 673 | | Forks | 9,421 | 51 | | Open issues | 84 | 11 | | Language | - | Go | | Adopt for | The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | GPL-3.0 | | Categories | LLM Frameworks, Model Training, Inference & Serving, Evaluation & Observability | LLM Frameworks, Inference & Serving, Computer Vision | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-course](/tools/mlabonne-llm-course.md) | [onWatch](/tools/onllm-dev-onwatch.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Active (82%) | | Days since push | 155d | 22d | | Open issues (now) | 84 | 11 | | Owner type | User | Organization | | Security scan | No lockfile | 27 low (27 low) | | Full report | [trust report](/tools/mlabonne-llm-course/trust.md) | [trust report](/tools/onllm-dev-onwatch/trust.md) | ## Decision facts: llm-course - **Requirements:** Course materials are available in Colab notebooks; access requires a Google account - **Adopt for:** The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to - **License detail:** Apache-2.0 ## Choose when ### Choose llm-course if… - License: llm-course is Apache-2.0, onWatch is GPL-3.0. - Requirements: Course materials are available in Colab notebooks; access requires a Google account. - Tags unique to llm-course: colab-notebooks, machine-learning, course, large-language-models. - Also covers Model Training, Evaluation & Observability. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ### Choose onWatch if… - License: onWatch is GPL-3.0, llm-course is Apache-2.0. - Tags unique to onWatch: ai-api-monitoring, api-monitoring, codex, antigravity. - Also covers Computer Vision. - onWatch ships Docker support for self-hosted deployment. ## When NOT to use llm-course - - If you only require a quick introduction to LLMs without deep dive into core components - - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ## When NOT to use onWatch - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## Common questions ### What is the difference between llm-course and onWatch? llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. onWatch: Track AI API quotas across Synthetic, Z.ai, Anthropic (Claude Code), Codex, GitHub Copilot & Antigravity in real time. Lightweight background daemon (<50MB RAM), SQLite storage, Material Design 3 dash. See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-course over onWatch? Choose llm-course over onWatch when License: llm-course is Apache-2.0, onWatch is GPL-3.0; Requirements: Course materials are available in Colab notebooks; access requires a Google account; Tags unique to llm-course: colab-notebooks, machine-learning, course, large-language-models; Also covers Model Training, Evaluation & Observability; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I choose onWatch over llm-course? Choose onWatch over llm-course when License: onWatch is GPL-3.0, llm-course is Apache-2.0; Tags unique to onWatch: ai-api-monitoring, api-monitoring, codex, antigravity; Also covers Computer Vision; onWatch ships Docker support for self-hosted deployment. ### When should I avoid llm-course? - If you only require a quick introduction to LLMs without deep dive into core components - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ### When should I avoid onWatch? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### Is llm-course or onWatch more popular on GitHub? llm-course has more GitHub stars (80,839 vs 673). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-course and onWatch open source? Yes - both are open-source projects on GitHub (llm-course: Apache-2.0, onWatch: GPL-3.0). ### Where can I find alternatives to llm-course or onWatch? GraphCanon lists graph-backed alternatives at [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) and [onWatch alternatives](/tools/onllm-dev-onwatch/alternatives) ([llm-course markdown twin](/tools/mlabonne-llm-course/alternatives.md), [onWatch markdown twin](/tools/onllm-dev-onwatch/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/mlabonne-llm-course-vs-onllm-dev-onwatch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-course or onWatch? llm-course: Slowing. onWatch: 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 llm-course and onWatch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-course trust report](/tools/mlabonne-llm-course/trust); [onWatch trust report](/tools/onllm-dev-onwatch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=mlabonne-llm-course`](/api/graphcanon/graph?tool=mlabonne-llm-course) - 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/_