--- title: "hopsworks vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/logicalclocks-hopsworks-vs-tensorchord-awesome-llmops" tools: ["logicalclocks-hopsworks", "tensorchord-awesome-llmops"] --- # hopsworks vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick hopsworks if hopsworks, an ML platform with robust data management and model serving capabilities, supports multiple cloud environments like AWS, Azure, and GCP; 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. [hopsworks](https://hopsworks.ai) reports 1.3k GitHub stars, 160 forks, and 16 open issues, last pushed Feb 10, 2025. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 993 forks, and 247 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [hopsworks's repository](https://github.com/logicalclocks/hopsworks) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [hopsworks](/tools/logicalclocks-hopsworks.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | Data-Intensive AI platform with Feature Store | An awesome & curated list of best LLMOps tools for developers | | Stars | 1,302 | 5,915 | | Forks | 160 | 993 | | Open issues | 16 | 247 | | Language | Java | Shell | | Adopt for | Hopsworks, an ML platform with robust data management and model serving capabilities, supports multiple cloud environments like AWS, Azure, and GCP. | 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. | | Persona | - | - | | Runtime | - | - | | License | AGPL-3.0 | CC0-1.0 | | Categories | Evaluation & Observability, Inference & Serving, Model Training | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hopsworks](/tools/logicalclocks-hopsworks.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 539d | 91d | | Open issues (now) | 16 | 247 | | Stars delta | Unknown | +28 (30d) | | Open issues delta | Unknown | +66 (30d) | | Full report | [trust report](/tools/logicalclocks-hopsworks/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: hopsworks - **Adopt for:** Hopsworks, an ML platform with robust data management and model serving capabilities, supports multiple cloud environments like AWS, Azure, and GCP. ## 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. ## Choose when ### Choose hopsworks if… - hopsworks is primarily Java; Awesome-LLMOps is Shell. - License: hopsworks is AGPL-3.0, Awesome-LLMOps is CC0-1.0. - Tags unique to hopsworks: aws, azure, feature-store, gcp. - When project requirements include a comprehensive feature store for AI applications ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; hopsworks is Java. - License: Awesome-LLMOps is CC0-1.0, hopsworks is AGPL-3.0. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops. - Also covers Computer Vision, Data & Retrieval, LLM Frameworks, Speech & Audio. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ## When NOT to use hopsworks - If developers prefer a tool requiring less computational resources to install - In scenarios where the preferred language is not Java and compatibility is an issue ## 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. ## Common questions ### What is the difference between hopsworks and Awesome-LLMOps? hopsworks: Data-Intensive AI platform with Feature Store. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories. ### When should I choose hopsworks over Awesome-LLMOps? Choose hopsworks over Awesome-LLMOps when hopsworks is primarily Java; Awesome-LLMOps is Shell; License: hopsworks is AGPL-3.0, Awesome-LLMOps is CC0-1.0; Tags unique to hopsworks: aws, azure, feature-store, gcp; When project requirements include a comprehensive feature store for AI applications. ### When should I choose Awesome-LLMOps over hopsworks? Choose Awesome-LLMOps over hopsworks when Awesome-LLMOps is primarily Shell; hopsworks is Java; License: Awesome-LLMOps is CC0-1.0, hopsworks is AGPL-3.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops; Also covers Computer Vision, Data & Retrieval, LLM Frameworks, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### When should I avoid hopsworks? If developers prefer a tool requiring less computational resources to install In scenarios where the preferred language is not Java and compatibility is an issue ### 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. ### Is hopsworks or Awesome-LLMOps more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,915 vs 1,302). Stars measure visibility, not whether either tool fits your constraints. ### Are hopsworks and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (hopsworks: AGPL-3.0, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to hopsworks or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [hopsworks alternatives](/tools/logicalclocks-hopsworks/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([hopsworks markdown twin](/tools/logicalclocks-hopsworks/alternatives.md), [Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/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/logicalclocks-hopsworks-vs-tensorchord-awesome-llmops.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hopsworks or Awesome-LLMOps? hopsworks: Dormant. Awesome-LLMOps: Slowing. 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 hopsworks and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hopsworks trust report](/tools/logicalclocks-hopsworks/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=logicalclocks-hopsworks`](/api/graphcanon/graph?tool=logicalclocks-hopsworks) - 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/_