Asynq vs Celery
Asynq
github.comCelery
docs.celeryq.devSide by side
| Figure | Asynq | Celery |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| macOS | — | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Task Queue Software | 7.9 (#15) | 8.1 (#11) |
| Monitoring | The project lists Prometheus metrics integration and a web UI and CLI for inspecting and remotely controlling queues and tasks. | Celery provides commands to inspect and manage worker nodes and documents monitoring tools including Flower and event utilities. |
| Purpose | Asynq is a Go library for queueing tasks and processing them asynchronously with workers, backed by Redis. | Celery is a distributed task queue focused on real-time processing that also supports task scheduling. |
| Scaling | The project describes using multiple worker servers and brokers for high availability and horizontal scaling. | A Celery system can use multiple workers and brokers to support high availability and horizontal scaling. |
| Scheduling | Asynq supports scheduled and periodic tasks. | Celery Beat starts tasks at regular intervals for execution by available worker nodes. |
| Security | The GitHub security page says the project has no security policy file and no published security advisories. | Celery advises treating it as an unsafe component and recommends restricting broker access; supported broker backends can use SSL encryption and authentication. |
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.