Apache AirflowOpen-source platform to programmatically author, schedule, and monitor workflows.
vs
Ray ServeScalable model serving library for building online inference APIs
Comparison · MLOps Platforms · weekly run Nov 2
Apache Airflow vs Ray Serve
Which one do AI engines recommend, and how do they differ on price, fit and features?
The short answer
Apache Airflow is the stronger AI pick in MLOps Platforms: it has a visibility score of 5.2 versus 0 for Ray Serve.
Choose Apache Airflow if…
- You want the brand AI engines rank higher (5.2 vs 0)
- free and open-source under Apache 2.0 license
- recommended as orchestration in practical vendor-neutral stacks
Choose Ray Serve if…
- You need SOC 2 Type II or ISO/IEC 27001 coverage
AI visibility side by side
Latest weekly run · Nov 2
| Metric | Apache Airflow | Ray Serve |
|---|---|---|
| Visibility score | 5.2% | 0% |
| Change vs last week | New | New |
| Rank in MLOps Platforms | #18 | #61 |
| Share of voice | 1.5% | 0% |
| Mentions this run | 10 | 0 |
| Positive in AI answers | 40% | — |
The stronger value in each row is marked with a ✓.
Visibility score trend
Weekly runs over the last 60 days
Apache Airflow 0pp · Ray Serve 0pp over 0 weekly runs.
Engine by engine
Apache Airflow leads on 3 of 3 engines
ChatGPT
AI Mode
Perplexity
Apache AirflowRay Serve
Head to head in AI answers
0 engine answers name both brands
0Apache Airflow ranked higher
0Ray Serve ranked higher
Features, pricing & fit
| Dimension | Apache Airflow | Ray Serve |
|---|---|---|
| Deployment | Self-hosted, installed via PyPI as the apache-airflow package or deployed from Docker images. | Self-managed on a laptop, in a Docker container, on a managed Ray cluster, or on Kubernetes via KubeRay |
| Integrations | 98+ | — |
| Compliance | — | SOC 2 Type II, ISO/IEC 27001 |
| Support | — | Ray Community Slack, Hands-on training and expert support from the Anyscale team that created Ray |
| Founded | 2015 | — |
| Headquarters | Wilmington, Delaware | San Francisco, California |
| Funding | — | Series C |
| Best for | Teams that run both data engineering and data science workflows and want to define them in Python. | Python teams serving machine learning models, including LLMs, as online inference APIs that need independent scaling |
Pros and cons
Themes from the AI answers that mention each brand
Apache Airflow
- free and open-source under Apache 2.0 license
- recommended as orchestration in practical vendor-neutral stacks
Ray Serve
Profile in progress.
Data collected ·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run
Cite as: SlateIndex, “Apache Airflow vs Ray Serve”, https://slateindex.ai/compare/apache-airflow-vs-ray-serve, data of Nov 2, 2026. Licence: CC BY 4.0.