head to head · open source
airflow vs Airbyte
airflow has 47,301 GitHub stars, 18,202 forks, 1,831 open issues and last shipped yesterday. Airbyte has 22,282 stars, 5,420 forks, 2,395 open issues and last shipped yesterday. airflow leads on adoption by 112% (47,301 vs 22,282 stars). airflow is written in Python under Apache-2.0; Airbyte is written in Python under a custom or non-standard licence. airflow has attracted 38% as many forks as stars, Airbyte 24%. airflow was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 4 topic tags (data-engineering, data-integration, elt, etl), so they are genuine substitutes rather than adjacent tools.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
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Side by side
| airflow | Airbyte | |
|---|---|---|
| GitHub stars | ★ 47K | ★ 22K |
| License | Apache-2.0 | Custom / other |
| Written in | Python | Python |
| Last push | 2026-10-28 | 2026-10-28 |
| Forks | ⑂ 18K | ⑂ 5.4K |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick airflow if
- You weight community size — 47K stars and counting
- You want the Apache-2.0 license terms
- Your stack matches Python
- You value the larger contributor base for long-term maintenance
pick Airbyte if
- You want the Airbyte feature set and don't need the biggest community
- You prefer the Custom / other license terms
- Your stack matches Python
- You evaluated both and Airbyte fits your workflow better
About airflow
Apache Airflow Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. Use Airflow to author workflows (Dags) that orchestrate tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on Dags a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when…
read the full airflow overview →
About Airbyte
Open source data movement for ELT pipelines and AI agents — from APIs, databases & files to warehouses, lakes, and AI applications We believe that only an open source solution to data movement can cover the long tail of data sources while empowering data engineers to customize existing connectors. Our ultimate vision is to help you move data from any source to any destination — whether that destination is a data warehouse, a data lake, or an AI agent. Airbyte provides a catalog of 600+ connectors for APIs, databases, data warehouses, data lakes, and AI applications. Screenshot taken from Airbyte Cloud . Pick the…
read the full Airbyte overview →
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Frequently asked questions
Is airflow or Airbyte more popular?
airflow has 47,301 GitHub stars and Airbyte has 22,282. airflow has the larger community by that measure.
Are airflow and Airbyte free?
Both are open source. airflow is licensed under Apache-2.0, and Airbyte has no licence declared in this registry. Both are free to self-host.
What is the difference between airflow and Airbyte?
airflow is written in Python and Airbyte in Python. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, airflow or Airbyte?
Choose airflow if you want the larger community (47,301 stars) or its Apache-2.0 licence terms. Choose Airbyte if its feature set, stack or Custom / other licence fits better. Both are self-hostable.