--- title: "awesome-mlops vs hopsworks" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kelvins-awesome-mlops-vs-logicalclocks-hopsworks" tools: ["kelvins-awesome-mlops", "logicalclocks-hopsworks"] --- # awesome-mlops vs hopsworks *GraphCanon updated Aug 4, 2026* ## Verdict Pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML; pick hopsworks if hopsworks, an ML platform with robust data management and model serving capabilities, supports multiple cloud environments like AWS, Azure, and GCP. [awesome-mlops](https://github.com/kelvins/awesome-mlops) reports 5.2k GitHub stars, 762 forks, and 71 open issues, last pushed Apr 29, 2026. [hopsworks](https://hopsworks.ai) has 1.3k stars, 160 forks, and 16 open issues, last pushed Feb 10, 2025. Figures are from public GitHub metadata via [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops) and [hopsworks's repository](https://github.com/logicalclocks/hopsworks). | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [hopsworks](/tools/logicalclocks-hopsworks.md) | | --- | --- | --- | | Tagline | A curated list of awesome MLOps tools. | Data-Intensive AI platform with Feature Store | | Stars | 5,229 | 1,302 | | Forks | 762 | 160 | | Open issues | 71 | 16 | | Language | Python | Java | | Adopt for | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. | Hopsworks, an ML platform with robust data management and model serving capabilities, supports multiple cloud environments like AWS, Azure, and GCP. | | Persona | - | - | | Runtime | - | - | | License | - | AGPL-3.0 | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | [hopsworks](/tools/logicalclocks-hopsworks.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 97d | 539d | | Open issues (now) | 71 | 16 | | Owner type | User | Organization | | Full report | [trust report](/tools/kelvins-awesome-mlops/trust.md) | [trust report](/tools/logicalclocks-hopsworks/trust.md) | ## Shared compatibility - **Python**: [awesome-mlops](/tools/kelvins-awesome-mlops.md) - Python runtime; [hopsworks](/tools/logicalclocks-hopsworks.md) - Python runtime ## Decision facts: awesome-mlops - **Adopt for:** Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. ## 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. ## Choose when ### Choose awesome-mlops if… - awesome-mlops is primarily Python; hopsworks is Java. - Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning. - Also covers Developer Tools. - You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### Choose hopsworks if… - hopsworks is primarily Java; awesome-mlops is Python. - Tags unique to hopsworks: aws, azure, feature-store, gcp. - When project requirements include a comprehensive feature store for AI applications ## When NOT to use awesome-mlops - In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform. - Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings. ## 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 ## Common questions ### What is the difference between awesome-mlops and hopsworks? awesome-mlops: A curated list of awesome MLOps tools.. hopsworks: Data-Intensive AI platform with Feature Store. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-mlops over hopsworks? Choose awesome-mlops over hopsworks when awesome-mlops is primarily Python; hopsworks is Java; Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning; Also covers Developer Tools; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### When should I choose hopsworks over awesome-mlops? Choose hopsworks over awesome-mlops when hopsworks is primarily Java; awesome-mlops is Python; Tags unique to hopsworks: aws, azure, feature-store, gcp; When project requirements include a comprehensive feature store for AI applications. ### When should I avoid awesome-mlops? In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform. Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings. ### 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 ### Is awesome-mlops or hopsworks more popular on GitHub? awesome-mlops has more GitHub stars (5,229 vs 1,302). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-mlops and hopsworks open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to awesome-mlops or hopsworks? GraphCanon lists graph-backed alternatives at [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) and [hopsworks alternatives](/tools/logicalclocks-hopsworks/alternatives) ([awesome-mlops markdown twin](/tools/kelvins-awesome-mlops/alternatives.md), [hopsworks markdown twin](/tools/logicalclocks-hopsworks/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/kelvins-awesome-mlops-vs-logicalclocks-hopsworks.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-mlops or hopsworks? awesome-mlops: Slowing. hopsworks: 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-mlops and hopsworks? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-mlops trust report](/tools/kelvins-awesome-mlops/trust); [hopsworks trust report](/tools/logicalclocks-hopsworks/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kelvins-awesome-mlops`](/api/graphcanon/graph?tool=kelvins-awesome-mlops) - 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/_