Comparison
awesome-open-mlops vs awesome-mlops
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 awesome-mlops if awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling.
Markdown twin · awesome-open-mlops alternatives · awesome-mlops alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | awesome-open-mlops | awesome-mlops |
|---|---|---|
| Maintenance | Dormant (442d since push) As of 2w · github_public_v1 | Dormant (621d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- awesome-mlops
- A curated list of references for MLOps
Stars
- awesome-open-mlops
- 482
- awesome-mlops
- 14k
Forks
- awesome-open-mlops
- 54
- awesome-mlops
- 2.1k
Open issues
- awesome-open-mlops
- 6
- awesome-mlops
- 44
Language
- awesome-open-mlops
- -
- awesome-mlops
- -
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.
- awesome-mlops
- awesome-mlops curates MLOps resources focusing on diverse deployment strategies and tooling.
Persona
- awesome-open-mlops
- -
- awesome-mlops
- -
Runtime
- awesome-open-mlops
- -
- awesome-mlops
- -
License
- awesome-open-mlops
- Apache 2.0 licensed, compatible with other Apache software, promoting free use in both commercial and non-commercial contexts.
- awesome-mlops
- -
Last pushed
- awesome-open-mlops
- May 19, 2025
- awesome-mlops
- Nov 21, 2024
Categories
- awesome-open-mlops
- Inference & Serving
- awesome-mlops
- Inference & Serving, Model Training
Trust and health
Days since push
- awesome-open-mlops
- 442d
- awesome-mlops
- 621d
Open issues (now)
- awesome-open-mlops
- 6
- awesome-mlops
- 44
Owner type
- awesome-open-mlops
- Organization
- awesome-mlops
- User
Full report
- awesome-open-mlops
- Trust report
- awesome-mlops
- Trust report
Choose awesome-open-mlops if…
- 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, infrastructure.
- 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 awesome-mlops if…
- Tags unique to awesome-mlops: ai, data-science, engineering, federated-learning.
- Also covers Model Training.
- If you need references covering online training and inference service architecture patterns, consider awesome-mlops.
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.
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 Aug 4, 2026
- GitHub forks (fuzzylabs/awesome-open-mlops) · observed Aug 4, 2026
- Last push (fuzzylabs/awesome-open-mlops) · observed May 19, 2025
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (visenger/awesome-mlops) · observed Aug 4, 2026
- GitHub forks (visenger/awesome-mlops) · observed Aug 4, 2026
- Last push (visenger/awesome-mlops) · observed Nov 21, 2024
- License file (unknown) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-open-mlops 482 · awesome-mlops 14k (synced Aug 4, 2026).
Common questions
- What is the difference between awesome-open-mlops and awesome-mlops?
- awesome-open-mlops: Model deployment and serving guide with open-source MLOps tools. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-open-mlops over awesome-mlops?
- Choose awesome-open-mlops over awesome-mlops when 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, infrastructure; 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 awesome-mlops over awesome-open-mlops?
- Choose awesome-mlops over awesome-open-mlops when Tags unique to awesome-mlops: ai, data-science, engineering, federated-learning; Also covers Model Training; If you need references covering online training and inference service architecture patterns, consider awesome-mlops.
- 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 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 awesome-open-mlops or awesome-mlops more popular on GitHub?
- awesome-mlops has more GitHub stars (14,127 vs 482). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-open-mlops and awesome-mlops open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to awesome-open-mlops or awesome-mlops?
- GraphCanon lists graph-backed alternatives at awesome-open-mlops alternatives and awesome-mlops alternatives (awesome-open-mlops markdown twin, awesome-mlops 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 awesome-mlops?
- awesome-open-mlops: 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 awesome-open-mlops and awesome-mlops?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-open-mlops trust report; awesome-mlops trust report.