Comparison
airflow vs awesome-mlops
Verdict
Pick airflow if apache Airflow is a Python-based orchestrator for scheduling and monitoring workflows, suitable for tasks that require flexible DAG (Directed Acyclic Graph) definitions; pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.
Markdown twin · airflow alternatives · awesome-mlops alternatives
GraphCanon updated Sep 14, 2026
9views this month
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Trust & integrity
| Signal | airflow | awesome-mlops |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 14, 2026 · github_public_v1 | Active (18d since push) As of Sep 4, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 14, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 4, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 15, 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
- airflow
- A platform to programmatically author, schedule, and monitor workflows
- awesome-mlops
- A curated list of awesome MLOps tools.
Stars
- airflow
- 47k
- awesome-mlops
- 5.3k
Forks
- airflow
- 18k
- awesome-mlops
- 775
Open issues
- airflow
- 2.1k
- awesome-mlops
- 82
Language
- airflow
- Python
- awesome-mlops
- Python
Adopt for
- airflow
- Apache Airflow is a Python-based orchestrator for scheduling and monitoring workflows, suitable for tasks that require flexible DAG (Directed Acyclic Graph) definitions.
- awesome-mlops
- Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.
Persona
- airflow
- -
- awesome-mlops
- -
Runtime
- airflow
- -
- awesome-mlops
- -
License
- airflow
- Apache-2.0
- awesome-mlops
- -
Last pushed
- airflow
- Sep 14, 2026
- awesome-mlops
- Aug 17, 2026
Categories
- airflow
- Developer Tools
- awesome-mlops
- Developer Tools, Evaluation & Observability, Inference & Serving, Model Training
Trust and health
Maintenance
- airflow
- Very active (96%)
- awesome-mlops
- Active (82%)
Days since push
- airflow
- 0d
- awesome-mlops
- 18d
Open issues (now)
- airflow
- 2.1k
- awesome-mlops
- 82
Stars delta
- airflow
- +419 (30d)
- awesome-mlops
- +36 (30d)
Open issues delta
- airflow
- +262 (30d)
- awesome-mlops
- +11 (30d)
Owner type
- airflow
- Organization
- awesome-mlops
- User
Full report
- airflow
- Trust report
- awesome-mlops
- Trust report
Shared compatibility
- Python · airflow: Python runtime · awesome-mlops: Python runtime
Choose airflow if…
- Tags unique to airflow: airflow, apache, automation, dag.
- airflow ships Docker support for self-hosted deployment.
- If you need to model complex workflow dependency graphs with Directed Acyclic Graphs (DAGs).
When NOT to use airflow
- Avoid if you require Windows as the primary execution environment without using WSL2.
- If your project strictly adheres to MariaDB for database management, Airflow is not recommended because it is neither tested nor supported by the tool.
Choose awesome-mlops if…
- Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning.
- Also covers Evaluation & Observability, Inference & Serving, Model Training.
- You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (apache/airflow) · observed Sep 14, 2026
- GitHub forks (apache/airflow) · observed Sep 14, 2026
- Last push (apache/airflow) · observed Sep 14, 2026
- License file (Apache-2.0) · observed Sep 14, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (kelvins/awesome-mlops) · observed Sep 4, 2026
- GitHub forks (kelvins/awesome-mlops) · observed Sep 4, 2026
- Last push (kelvins/awesome-mlops) · observed Aug 17, 2026
- License file (unknown) · observed Sep 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: airflow 47k · awesome-mlops 5.3k (synced Sep 14, 2026).
Common questions
- What is the difference between airflow and awesome-mlops?
- airflow: A platform to programmatically author, schedule, and monitor workflows. awesome-mlops: A curated list of awesome MLOps tools.. See the comparison table for live GitHub stats and shared categories.
- When should I choose airflow over awesome-mlops?
- Choose airflow over awesome-mlops when Tags unique to airflow: airflow, apache, automation, dag; airflow ships Docker support for self-hosted deployment; If you need to model complex workflow dependency graphs with Directed Acyclic Graphs (DAGs).
- When should I choose awesome-mlops over airflow?
- Choose awesome-mlops over airflow when Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning; Also covers Evaluation & Observability, Inference & Serving, Model Training; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
- When should I avoid airflow?
- Avoid if you require Windows as the primary execution environment without using WSL2. If your project strictly adheres to MariaDB for database management, Airflow is not recommended because it is neither tested nor supported by the tool.
- 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.
- Is airflow or awesome-mlops more popular on GitHub?
- airflow has more GitHub stars (46,844 vs 5,265). Stars measure visibility, not whether either tool fits your constraints.
- Are airflow and awesome-mlops open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to airflow or awesome-mlops?
- GraphCanon lists graph-backed alternatives at airflow alternatives and awesome-mlops alternatives (airflow 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, airflow or awesome-mlops?
- airflow: Very active. awesome-mlops: 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 airflow and awesome-mlops?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: airflow trust report; awesome-mlops trust report.