--- title: "graph vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/cosmosgl-graph-vs-visenger-awesome-mlops" tools: ["cosmosgl-graph", "visenger-awesome-mlops"] --- # graph vs awesome-mlops *GraphCanon updated Jul 12, 2026* ## Verdict Pick graph when pricing: Free and open-source under the MIT license.; pick awesome-mlops when tags unique to awesome-mlops: ai, data-science, devops, engineering. [graph](https://cosmos.gl) reports 1.2k GitHub stars, 83 forks, and 18 open issues, last pushed Jul 11, 2026. [awesome-mlops](https://ml-ops.org) has 14k stars, 2.1k forks, and 42 open issues, last pushed Nov 21, 2024. Figures are from public GitHub metadata via [graph's repository](https://github.com/cosmosgl/graph) and [awesome-mlops's repository](https://github.com/visenger/awesome-mlops). | | [graph](/tools/cosmosgl-graph.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Tagline | GPU-accelerated force graph layout and rendering | A curated list of references for MLOps | | Stars | 1,193 | 13,952 | | Forks | 83 | 2,072 | | Open issues | 18 | 42 | | Language | TypeScript | - | | Adopt for | CosmosGL/graph provides GPU-accelerated techniques for creating and rendering force-directed layouts. This makes it particularly apt for users who need to visualize complex networks efficiently. | - | | Persona | - | - | | Runtime | - | - | | License | MIT License | - | | Categories | Data & Retrieval, Vector Databases | Inference & Serving, Model Training, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [graph](/tools/cosmosgl-graph.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 597d | | Open issues (now) | 18 | 42 | | Owner type | Organization | User | | Full report | [trust report](/tools/cosmosgl-graph/trust.md) | [trust report](/tools/visenger-awesome-mlops/trust.md) | ## Decision facts: graph - **Pricing:** freemium - Free and open-source under the MIT license. - **Requirements:** Requires a WebGL-supported environment - **Adopt for:** CosmosGL/graph provides GPU-accelerated techniques for creating and rendering force-directed layouts. This makes it particularly apt for users who need to visualize complex networks efficiently. - **License detail:** MIT License ## Choose when ### Choose graph if… - Pricing: Free and open-source under the MIT license.. - Requirements: Requires a WebGL-supported environment. - Tags unique to graph: embeddings, force, graph, network. - Also covers Data & Retrieval. - - When you require rapid visualization of large, complex network structures due to its GPU acceleration ### Choose awesome-mlops if… - Tags unique to awesome-mlops: ai, data-science, devops, engineering. - Also covers Inference & Serving, Model Training. - More GitHub stars (14k vs 1.2k) - visibility, not fit. ## When NOT to use graph - - If your project does not involve visualizing complex networks as this tool's forte lies in force-directed graphical representations - - When working with systems or frameworks that do not support WebGL, since CosmosGL/graph relies on it for rendering ## When NOT to use awesome-mlops - Last GitHub push was 597 days ago (dormant maintenance, Nov 21, 2024). Validate activity before betting a new project on awesome-mlops. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between graph and awesome-mlops? graph: GPU-accelerated force graph layout and rendering. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories. ### When should I choose graph over awesome-mlops? Choose graph over awesome-mlops when Pricing: Free and open-source under the MIT license.; Requirements: Requires a WebGL-supported environment; Tags unique to graph: embeddings, force, graph, network; Also covers Data & Retrieval; - When you require rapid visualization of large, complex network structures due to its GPU acceleration. ### When should I choose awesome-mlops over graph? Choose awesome-mlops over graph when Tags unique to awesome-mlops: ai, data-science, devops, engineering; Also covers Inference & Serving, Model Training; More GitHub stars (14k vs 1.2k) - visibility, not fit. ### When should I avoid graph? - If your project does not involve visualizing complex networks as this tool's forte lies in force-directed graphical representations - When working with systems or frameworks that do not support WebGL, since CosmosGL/graph relies on it for rendering ### When should I avoid awesome-mlops? Last GitHub push was 597 days ago (dormant maintenance, Nov 21, 2024). Validate activity before betting a new project on awesome-mlops. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is graph or awesome-mlops more popular on GitHub? awesome-mlops has more GitHub stars (13,952 vs 1,193). Stars measure visibility, not whether either tool fits your constraints. ### Are graph and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to graph or awesome-mlops? GraphCanon lists graph-backed alternatives at [graph alternatives](/tools/cosmosgl-graph/alternatives) and [awesome-mlops alternatives](/tools/visenger-awesome-mlops/alternatives) ([graph markdown twin](/tools/cosmosgl-graph/alternatives.md), [awesome-mlops markdown twin](/tools/visenger-awesome-mlops/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/cosmosgl-graph-vs-visenger-awesome-mlops.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, graph or awesome-mlops? graph: Very active. awesome-mlops: Dormant. 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 graph and awesome-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [graph trust report](/tools/cosmosgl-graph/trust); [awesome-mlops trust report](/tools/visenger-awesome-mlops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=cosmosgl-graph`](/api/graphcanon/graph?tool=cosmosgl-graph) - 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/_