Apache Airflow vs. Seldon
A side-by-side comparison based on G2 review data.
| Metric | Apache Airflow | Seldon |
|---|---|---|
| Overall rating | 4.4 | 4.3 |
| Review count | 130 | 11 |
| Ease of use | 8.7 | 8.2 |
| Ease of setup | 8.2 | 7.1 |
| Quality of support | 8.5 | 9.3 |
| Meets requirements | 9.2 | 9.6 |
| NPS | 62.5 | 54.545455 |
| Small Business | 22.8% | 36.4% |
| Mid-Market | 45.7% | 18.2% |
| Enterprise | 31.5% | 45.5% |
| Total page views | 451 | 62 |
| Total LLM citations | 1,881 | 128 |
Apache Airflow
Apache Airflow is an open-source platform designed for authoring, scheduling, and monitoring complex workflows. Developed in Python, it enables users to define workflows as code, facilitating dynamic pipeline generation and seamless integration with various technologies. Airflow's modular architecture and message queue system allow it to scale efficiently, managing workflows from single machines to large-scale distributed systems. Its user-friendly web interface provides comprehensive monitoring and management capabilities, offering clear insights into task statuses and execution logs.
- Ease of Use
- User Interface
- Flexibility
- Integrations
- Automation
- Easy Integrations
Seldon
Seldon gets machine learning models to production faster, in the most reliable way. Front-end deployment of models, explainers and canaries means users can deploy ML models and testing can be done in live environments. Metrics and dashboards can monitor models to improve performance and rapidly communicate errors for easy debugging. Model explainers mean you can understand and adjust what features are influencing the model and anomaly detection can flag drifts in data.