--- title: "hopsworks vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/logicalclocks-hopsworks-vs-visenger-awesome-mlops" tools: ["logicalclocks-hopsworks", "visenger-awesome-mlops"] --- # hopsworks vs awesome-mlops *GraphCanon updated Aug 4, 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-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. [hopsworks](https://hopsworks.ai) reports 1.3k GitHub stars, 160 forks, and 16 open issues, last pushed Feb 10, 2025. [awesome-mlops](https://ml-ops.org) has 14k stars, 2.1k forks, and 44 open issues, last pushed Nov 21, 2024. Figures are from public GitHub metadata via [hopsworks's repository](https://github.com/logicalclocks/hopsworks) and [awesome-mlops's repository](https://github.com/visenger/awesome-mlops). | | [hopsworks](/tools/logicalclocks-hopsworks.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Tagline | Data-Intensive AI platform with Feature Store | A curated list of references for MLOps | | Stars | 1,302 | 14,127 | | Forks | 160 | 2,101 | | Open issues | 16 | 44 | | Language | Java | - | | Adopt for | Hopsworks, an ML platform with robust data management and model serving capabilities, supports multiple cloud environments like AWS, Azure, and GCP. | awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. | | Persona | - | - | | Runtime | - | - | | License | AGPL-3.0 | - | | Categories | Evaluation & Observability, Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hopsworks](/tools/logicalclocks-hopsworks.md) | [awesome-mlops](/tools/visenger-awesome-mlops.md) | | --- | --- | --- | | Days since push | 539d | 621d | | Open issues (now) | 16 | 44 | | Owner type | Organization | User | | Full report | [trust report](/tools/logicalclocks-hopsworks/trust.md) | [trust report](/tools/visenger-awesome-mlops/trust.md) | ## Shared compatibility - **Python**: [hopsworks](/tools/logicalclocks-hopsworks.md) - Python runtime; [awesome-mlops](/tools/visenger-awesome-mlops.md) - Python runtime ## 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-mlops - **Adopt for:** awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling. ## Choose when ### Choose hopsworks if… - Tags unique to hopsworks: aws, azure, feature-store, gcp. - Also covers Evaluation & Observability. - When project requirements include a comprehensive feature store for AI applications ### Choose awesome-mlops if… - Tags unique to awesome-mlops: ai, data-science, devops, engineering. - If you need references covering online training and inference service architecture patterns, consider awesome-mlops. - More GitHub stars (14k vs 1.3k) - visibility, not fit. ## 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-mlops - Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list. - Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance. ## Common questions ### What is the difference between hopsworks and awesome-mlops? hopsworks: Data-Intensive AI platform with Feature Store. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories. ### When should I choose hopsworks over awesome-mlops? Choose hopsworks over awesome-mlops when Tags unique to hopsworks: aws, azure, feature-store, gcp; Also covers Evaluation & Observability; When project requirements include a comprehensive feature store for AI applications. ### When should I choose awesome-mlops over hopsworks? Choose awesome-mlops over hopsworks when Tags unique to awesome-mlops: ai, data-science, devops, engineering; If you need references covering online training and inference service architecture patterns, consider awesome-mlops; More GitHub stars (14k vs 1.3k) - visibility, not fit. ### 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-mlops? Avoid if focused solely on a single MLOps tool or framework as this is a broad resource list. Not suitable for those seeking end-to-end support beyond references, like hands-on deployment assistance. ### Is hopsworks or awesome-mlops more popular on GitHub? awesome-mlops has more GitHub stars (14,127 vs 1,302). Stars measure visibility, not whether either tool fits your constraints. ### Are hopsworks and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to hopsworks or awesome-mlops? GraphCanon lists graph-backed alternatives at [hopsworks alternatives](/tools/logicalclocks-hopsworks/alternatives) and [awesome-mlops alternatives](/tools/visenger-awesome-mlops/alternatives) ([hopsworks markdown twin](/tools/logicalclocks-hopsworks/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/logicalclocks-hopsworks-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, hopsworks or awesome-mlops? hopsworks: Dormant. 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 hopsworks and awesome-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hopsworks trust report](/tools/logicalclocks-hopsworks/trust); [awesome-mlops trust report](/tools/visenger-awesome-mlops/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/_