Comparison
awesome-open-mlops vs mlrun
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 mlrun if mLRun: Open-source MLOps platform for rapid ML application development integrated into CI/CD pipelines using Python.
Markdown twin · awesome-open-mlops alternatives · mlrun alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | awesome-open-mlops | mlrun |
|---|---|---|
| Maintenance | Dormant (442d since push) As of 2w · github_public_v1 | Very active (0d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings 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
- mlrun
- MLOps Platform for Building and Managing Continuous ML Applications
Stars
- awesome-open-mlops
- 482
- mlrun
- 1.7k
Forks
- awesome-open-mlops
- 54
- mlrun
- 315
Open issues
- awesome-open-mlops
- 6
- mlrun
- 110
Language
- awesome-open-mlops
- -
- mlrun
- Python
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.
- mlrun
- MLRun: Open-source MLOps platform for rapid ML application development integrated into CI/CD pipelines using Python.
Persona
- awesome-open-mlops
- -
- mlrun
- -
Runtime
- awesome-open-mlops
- -
- mlrun
- -
License
- awesome-open-mlops
- Apache 2.0 licensed, compatible with other Apache software, promoting free use in both commercial and non-commercial contexts.
- mlrun
- Apache-2.0
Last pushed
- awesome-open-mlops
- May 19, 2025
- mlrun
- Aug 2, 2026
Categories
- awesome-open-mlops
- Inference & Serving
- mlrun
- Developer Tools, Inference & Serving, Model Training
Trust and health
Maintenance
- awesome-open-mlops
- Dormant (18%)
- mlrun
- Very active (96%)
Days since push
- awesome-open-mlops
- 442d
- mlrun
- 0d
Open issues (now)
- awesome-open-mlops
- 6
- mlrun
- 110
OSV dependency advisories
- awesome-open-mlops
- No lockfile (source not queried)
- mlrun
- Published findings
Full report
- awesome-open-mlops
- Trust report
- mlrun
- 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, 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 mlrun if…
- Tags unique to mlrun: ci-cd, machine learning pipelines, serverless functions.
- Also covers Developer Tools, Model Training.
- Requires seamless integration of ML workflows into existing CI/CD environments
When NOT to use mlrun
- Lacks requirement for serverless function deployment with auto-scaling capabilities
- CI/CD integration is not a priority or already fully catered to by alternative tools
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 (mlrun/mlrun) · observed Aug 3, 2026
- GitHub forks (mlrun/mlrun) · observed Aug 3, 2026
- Last push (mlrun/mlrun) · observed Aug 2, 2026
- License file (Apache-2.0) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-open-mlops 482 · mlrun 1.7k (synced Aug 4, 2026).
Common questions
- What is the difference between awesome-open-mlops and mlrun?
- awesome-open-mlops: Model deployment and serving guide with open-source MLOps tools. mlrun: MLOps Platform for Building and Managing Continuous ML Applications. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-open-mlops over mlrun?
- Choose awesome-open-mlops over mlrun 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, 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 mlrun over awesome-open-mlops?
- Choose mlrun over awesome-open-mlops when Tags unique to mlrun: ci-cd, machine learning pipelines, serverless functions; Also covers Developer Tools, Model Training; Requires seamless integration of ML workflows into existing CI/CD environments.
- 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 mlrun?
- Lacks requirement for serverless function deployment with auto-scaling capabilities CI/CD integration is not a priority or already fully catered to by alternative tools
- Is awesome-open-mlops or mlrun more popular on GitHub?
- mlrun has more GitHub stars (1,690 vs 482). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-open-mlops and mlrun open source?
- Yes - both are open-source projects on GitHub (awesome-open-mlops: Apache-2.0, mlrun: Apache-2.0).
- Where can I find alternatives to awesome-open-mlops or mlrun?
- GraphCanon lists graph-backed alternatives at awesome-open-mlops alternatives and mlrun alternatives (awesome-open-mlops markdown twin, mlrun 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 mlrun?
- awesome-open-mlops: Dormant. mlrun: Very active. 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 mlrun?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-open-mlops trust report; mlrun trust report.