--- title: "awesome-open-mlops vs hopsworks" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/fuzzylabs-awesome-open-mlops-vs-logicalclocks-hopsworks" tools: ["fuzzylabs-awesome-open-mlops", "logicalclocks-hopsworks"] --- # awesome-open-mlops vs hopsworks *GraphCanon updated Oct 4, 2026* ## Verdict Pick awesome-open-mlops if awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs; 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-open-mlops](https://github.com/fuzzylabs/awesome-open-mlops) reports 483 GitHub stars, 55 forks, and 7 open issues, last pushed May 19, 2025. [hopsworks](https://hopsworks.ai) has 1.3k stars, 159 forks, and 16 open issues, last pushed Feb 10, 2025. Figures are from public GitHub metadata via [awesome-open-mlops's repository](https://github.com/fuzzylabs/awesome-open-mlops) and [hopsworks's repository](https://github.com/logicalclocks/hopsworks). | | [awesome-open-mlops](/tools/fuzzylabs-awesome-open-mlops.md) | [hopsworks](/tools/logicalclocks-hopsworks.md) | | --- | --- | --- | | Tagline | Model deployment and serving guide with open-source MLOps tools | Data-Intensive AI platform with Feature Store | | Stars | 483 | 1,307 | | Forks | 55 | 159 | | Open issues | 7 | 16 | | Language | - | Java | | Adopt for | awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs. | Hopsworks, an ML platform with robust data management and model serving capabilities, supports multiple cloud environments like AWS, Azure, and GCP. | | Persona | - | - | | Runtime | - | - | | License | Apache 2.0 licensed, compatible with other Apache software, promoting free use in both commercial and non-commercial contexts. | AGPL-3.0 | | Categories | Inference & Serving | Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-open-mlops](/tools/fuzzylabs-awesome-open-mlops.md) | [hopsworks](/tools/logicalclocks-hopsworks.md) | | --- | --- | --- | | Days since push | 502d | 595d | | Open issues (now) | 7 | 16 | | Stars delta | +1 (30d) | +5 (30d) | | Open issues delta | +1 (30d) | 0 (30d) | | Full report | [trust report](/tools/fuzzylabs-awesome-open-mlops/trust.md) | [trust report](/tools/logicalclocks-hopsworks/trust.md) | ## Decision facts: awesome-open-mlops - **Hosting:** unknown - No specific details available. - **Pricing:** freemium - `awesome-open-mlops` is freely accessible but depends on the community for updates and content contributions. No paid services are associated with this repository, making it purely a curated resource. - **Adopt for:** awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs. - **License detail:** Apache 2.0 licensed, compatible with other Apache software, promoting free use in both commercial and non-commercial contexts. ## 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-open-mlops if… - License: awesome-open-mlops is Apache-2.0, hopsworks is AGPL-3.0. - No specific details available. - Pricing: `awesome-open-mlops` is freely accessible but depends on the community for updates and content contributions. No paid services are associated with this repository, making it purely a curated resource.. - Tags unique to awesome-open-mlops: datascience, devops, infrastructure, machine-learning. - When seeking a comprehensive list of open-source models focused on deploying and serving ML models for REST API use cases ### Choose hopsworks if… - License: hopsworks is AGPL-3.0, awesome-open-mlops is Apache-2.0. - Tags unique to hopsworks: aws, azure, feature-store, gcp. - Also covers Evaluation & Observability, Model Training. - When project requirements include a comprehensive feature store for AI applications ## When NOT to use awesome-open-mlops - Avoid if you need proprietary or commercial MLOps solutions that offer enterprise support or features not covered by open-source projects - Not suitable for scenarios where model serving frameworks outside of the curated list, such as those under different licenses like AGPL-3.0 used by BodyworkML, are required ## 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-open-mlops and hopsworks? awesome-open-mlops: Model deployment and serving guide with open-source 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-open-mlops over hopsworks? Choose awesome-open-mlops over hopsworks when License: awesome-open-mlops is Apache-2.0, hopsworks is AGPL-3.0; No specific details available; Pricing: `awesome-open-mlops` is freely accessible but depends on the community for updates and content contributions. No paid services are associated with this repository, making it purely a curated resource.; Tags unique to awesome-open-mlops: datascience, devops, infrastructure, machine-learning; When seeking a comprehensive list of open-source models focused on deploying and serving ML models for REST API use cases. ### When should I choose hopsworks over awesome-open-mlops? Choose hopsworks over awesome-open-mlops when License: hopsworks is AGPL-3.0, awesome-open-mlops is Apache-2.0; Tags unique to hopsworks: aws, azure, feature-store, gcp; Also covers Evaluation & Observability, Model Training; When project requirements include a comprehensive feature store for AI applications. ### When should I avoid awesome-open-mlops? Avoid if you need proprietary or commercial MLOps solutions that offer enterprise support or features not covered by open-source projects Not suitable for scenarios where model serving frameworks outside of the curated list, such as those under different licenses like AGPL-3.0 used by BodyworkML, are required ### 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-open-mlops or hopsworks more popular on GitHub? hopsworks has more GitHub stars (1,307 vs 483). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-open-mlops and hopsworks open source? Yes - both are open-source projects on GitHub (awesome-open-mlops: Apache-2.0, hopsworks: AGPL-3.0). ### Where can I find alternatives to awesome-open-mlops or hopsworks? GraphCanon lists graph-backed alternatives at [awesome-open-mlops alternatives](/tools/fuzzylabs-awesome-open-mlops/alternatives) and [hopsworks alternatives](/tools/logicalclocks-hopsworks/alternatives) ([awesome-open-mlops markdown twin](/tools/fuzzylabs-awesome-open-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/fuzzylabs-awesome-open-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-open-mlops or hopsworks? awesome-open-mlops: Dormant. 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-open-mlops and hopsworks? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-open-mlops trust report](/tools/fuzzylabs-awesome-open-mlops/trust); [hopsworks trust report](/tools/logicalclocks-hopsworks/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=fuzzylabs-awesome-open-mlops`](/api/graphcanon/graph?tool=fuzzylabs-awesome-open-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/_