Celery vs Taskcluster
Celery
docs.celeryq.devTaskcluster
taskcluster.netSide by side
| Figure | Celery | Taskcluster |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | — | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Task Queue Software | 8.1 (#11) | 6.7 (#35) |
| Purpose | Celery is a distributed task queue focused on real-time processing that also supports task scheduling. | Taskcluster is an open-source task execution framework for building customized continuous integration and release processes. |
| Scheduling | Celery Beat starts tasks at regular intervals for execution by available worker nodes. | Scheduled tasks can run at specific times or days for jobs such as nightly builds and periodic maintenance. |
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.
Taskcluster
Taskcluster has no write-up on Specifiction yet; its figures are above.