--- title: "Awesome-LLMOps vs uptrain" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/tensorchord-awesome-llmops-vs-uptrain-ai-uptrain" tools: ["tensorchord-awesome-llmops", "uptrain-ai-uptrain"] --- # Awesome-LLMOps vs uptrain *GraphCanon updated Aug 20, 2026* ## Verdict Pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more; pick uptrain if upTrain, an open-source platform, evaluates and enhances Generative AI applications with preconfigured checks, root cause analysis, and actionable insights. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) reports 5.9k GitHub stars, 993 forks, and 247 open issues, last pushed May 21, 2026. [uptrain](https://uptrain.ai/) has 2.4k stars, 204 forks, and 58 open issues, last pushed Aug 18, 2024. Figures are from public GitHub metadata via [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps) and [uptrain's repository](https://github.com/uptrain-ai/uptrain). | | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | [uptrain](/tools/uptrain-ai-uptrain.md) | | --- | --- | --- | | Tagline | An awesome & curated list of best LLMOps tools for developers | Unified platform for evaluating and improving Generative AI applications | | Stars | 5,915 | 2,359 | | Forks | 993 | 204 | | Open issues | 247 | 58 | | Language | Shell | Python | | Adopt for | Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. | UpTrain, an open-source platform, evaluates and enhances Generative AI applications with preconfigured checks, root cause analysis, and actionable insights. | | Persona | - | - | | Runtime | - | - | | License | CC0-1.0 | The tool is available under the Apache-2.0 license, suitable for both free and commercial use with appropriate attribution. | | Categories | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | [uptrain](/tools/uptrain-ai-uptrain.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 91d | 731d | | Open issues (now) | 247 | 58 | | Stars delta | +28 (30d) | +4 (30d) | | Open issues delta | +66 (30d) | +3 (30d) | | Full report | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | [trust report](/tools/uptrain-ai-uptrain/trust.md) | ## Decision facts: Awesome-LLMOps - **Adopt for:** Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. ## Decision facts: uptrain - **Hosting:** self hosted - UpTrain can be installed on-premises using pip or accessed through a managed version. - **Adopt for:** UpTrain, an open-source platform, evaluates and enhances Generative AI applications with preconfigured checks, root cause analysis, and actionable insights. - **License detail:** The tool is available under the Apache-2.0 license, suitable for both free and commercial use with appropriate attribution. ## Choose when ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; uptrain is Python. - License: Awesome-LLMOps is CC0-1.0, uptrain is Apache-2.0. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - Also covers Computer Vision, Data & Retrieval, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### Choose uptrain if… - uptrain is primarily Python; Awesome-LLMOps is Shell. - License: uptrain is Apache-2.0, Awesome-LLMOps is CC0-1.0. - UpTrain can be installed on-premises using pip or accessed through a managed version. - Tags unique to uptrain: autoevaluation, evaluation, experimentation, hallucination-detection. - uptrain ships Docker support for self-hosted deployment. - - When you need to evaluate Generative AI applications across various use-cases including language models, code generation, and embeddings. ## When NOT to use Awesome-LLMOps - - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ## When NOT to use uptrain - - When your application does not require extensive monitoring or do not need insights into improving Generative AI performance through root cause analysis. - - If you prioritize a highly hands-off user experience without the capability to customize evaluation checks, consider using UpTrain's managed version instead of self-managing it. ## Common questions ### What is the difference between Awesome-LLMOps and uptrain? Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. uptrain: Unified platform for evaluating and improving Generative AI applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLMOps over uptrain? Choose Awesome-LLMOps over uptrain when Awesome-LLMOps is primarily Shell; uptrain is Python; License: Awesome-LLMOps is CC0-1.0, uptrain is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### When should I choose uptrain over Awesome-LLMOps? Choose uptrain over Awesome-LLMOps when uptrain is primarily Python; Awesome-LLMOps is Shell; License: uptrain is Apache-2.0, Awesome-LLMOps is CC0-1.0; UpTrain can be installed on-premises using pip or accessed through a managed version; Tags unique to uptrain: autoevaluation, evaluation, experimentation, hallucination-detection; uptrain ships Docker support for self-hosted deployment; - When you need to evaluate Generative AI applications across various use-cases including language models, code generation, and embeddings. ### When should I avoid Awesome-LLMOps? - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ### When should I avoid uptrain? - When your application does not require extensive monitoring or do not need insights into improving Generative AI performance through root cause analysis. - If you prioritize a highly hands-off user experience without the capability to customize evaluation checks, consider using UpTrain's managed version instead of self-managing it. ### Is Awesome-LLMOps or uptrain more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,915 vs 2,359). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMOps and uptrain open source? Yes - both are open-source projects on GitHub (Awesome-LLMOps: CC0-1.0, uptrain: Apache-2.0). ### Where can I find alternatives to Awesome-LLMOps or uptrain? GraphCanon lists graph-backed alternatives at [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) and [uptrain alternatives](/tools/uptrain-ai-uptrain/alternatives) ([Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/alternatives.md), [uptrain markdown twin](/tools/uptrain-ai-uptrain/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/tensorchord-awesome-llmops-vs-uptrain-ai-uptrain.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-LLMOps or uptrain? Awesome-LLMOps: Slowing. uptrain: 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 Awesome-LLMOps and uptrain? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust); [uptrain trust report](/tools/uptrain-ai-uptrain/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=tensorchord-awesome-llmops`](/api/graphcanon/graph?tool=tensorchord-awesome-llmops) - 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/_