arq vs Celery
arq
arq-docs.helpmanual.ioCelery
docs.celeryq.devSide by side
| Figure | arq | Celery |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| macOS | — | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 1 |
| Spec score · Task Queue Software | 7.7 (#19) | 8.1 (#11) |
| License | The PyPI project page lists the MIT License. | Celery is open source and licensed under the New BSD License. |
| Purpose | arq provides job queues and RPC in Python using asyncio and Redis. | Celery is a distributed task queue focused on real-time processing that also supports task scheduling. |
| Scheduling | Jobs can be deferred until a specified time or duration, and functions can be scheduled as cron jobs. | Celery Beat starts tasks at regular intervals for execution by available worker nodes. |
Plans
In their own words
Celery
Celery is a free, open-source distributed task queue for real-time processing and scheduled jobs. It sends task messages through a broker to worker processes, and systems can run multiple workers and brokers for high availability and horizontal scaling. Celery Beat starts tasks at regular intervals for available workers. Documented transports include RabbitMQ, Redis, Amazon SQS, Kafka and Google Pub/Sub, with different capability labels: RabbitMQ and Redis are stable, SQS is stable without monitoring or remote control, and Kafka is experimental without those features. Integrations are documented for Django, Pyramid, Pylons, Flask, web2py, Tornado and FastAPI. Commands inspect and manage workers, while Flower and event utilities provide monitoring options. Celery 5.6 supports Python 3.9 through 3.13 and PyPy 3.9 or newer. It is self-hosted, installable via PyPI or source, and licensed under the New BSD License.