Comparison
awesome-open-mlops vs hopsworks
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.
Markdown twin · awesome-open-mlops alternatives · hopsworks alternatives
GraphCanon updated Oct 4, 2026
vs
Trust & integrity
| Signal | awesome-open-mlops | hopsworks |
|---|---|---|
| Maintenance | Dormant (502d since push) As of Oct 4, 2026 · github_public_v1 | Dormant (595d since push) As of Sep 28, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Oct 4, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 28, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- awesome-open-mlops
- Model deployment and serving guide with open-source MLOps tools
- hopsworks
- Data-Intensive AI platform with Feature Store
Stars
- awesome-open-mlops
- 483
- hopsworks
- 1.3k
Forks
- awesome-open-mlops
- 55
- hopsworks
- 159
Open issues
- awesome-open-mlops
- 7
- hopsworks
- 16
Language
- awesome-open-mlops
- -
- hopsworks
- Java
Adopt for
- awesome-open-mlops
- awesome-open-mlops highlights open-source MLOps tools specifically for model deployment and serving, offering a guide curated by Fuzzy Labs.
- hopsworks
- Hopsworks, an ML platform with robust data management and model serving capabilities, supports multiple cloud environments like AWS, Azure, and GCP.
Persona
- awesome-open-mlops
- -
- hopsworks
- -
Runtime
- awesome-open-mlops
- -
- hopsworks
- -
License
- awesome-open-mlops
- Apache 2.0 licensed, compatible with other Apache software, promoting free use in both commercial and non-commercial contexts.
- hopsworks
- AGPL-3.0
Last pushed
- awesome-open-mlops
- May 19, 2025
- hopsworks
- Feb 10, 2025
Categories
- awesome-open-mlops
- Inference & Serving
- hopsworks
- Evaluation & Observability, Inference & Serving, Model Training
Trust and health
Days since push
- awesome-open-mlops
- 502d
- hopsworks
- 595d
Open issues (now)
- awesome-open-mlops
- 7
- hopsworks
- 16
Stars delta
- awesome-open-mlops
- +1 (30d)
- hopsworks
- +5 (30d)
Open issues delta
- awesome-open-mlops
- +1 (30d)
- hopsworks
- 0 (30d)
Full report
- awesome-open-mlops
- Trust report
- hopsworks
- Trust report
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
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
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 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (fuzzylabs/awesome-open-mlops) · observed Oct 4, 2026
- GitHub forks (fuzzylabs/awesome-open-mlops) · observed Oct 4, 2026
- Last push (fuzzylabs/awesome-open-mlops) · observed May 19, 2025
- License file (Apache-2.0) · observed Oct 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (logicalclocks/hopsworks) · observed Sep 28, 2026
- GitHub forks (logicalclocks/hopsworks) · observed Sep 28, 2026
- Last push (logicalclocks/hopsworks) · observed Feb 10, 2025
- License file (AGPL-3.0) · observed Sep 28, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-open-mlops 483 · hopsworks 1.3k (synced Oct 4, 2026).
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-mlopsis 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 and hopsworks alternatives (awesome-open-mlops markdown twin, hopsworks markdown twin), 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 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; hopsworks trust report.