Celery vs Faktory

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.

Faktory is a persistent background job system that works across programming languages. It includes a responsive web interface for viewing and managing jobs, and uses Redis as a child process for local storage, persisting data to its configured directory. The maker lists worker bindings for Node/JavaScript, Ruby, Go, Python, PHP, Elixir, Rust, Swift, Crystal, Haskell, Java/JVM,.NET, and Clojure. Linux RPM and DEB packages, macOS Homebrew installation, and a Docker image are documented. Faktory supports TLS for worker and HTTP ports, with clients authenticated by a configured global password. The OSS edition is free under AGPL and has no dedicated support. Enterprise features include cron jobs, unique and expiring jobs, Redis Gateway, StatsD metrics, queue throttling, batches or workflows, and job status. Enterprise production use requires a valid license and is limited to purchased connections: the first pack is $299 a month for 100 connections, and each additional pack is $99 a month for 100 connections. Faktory does not provide clustered high availability; persistence and failover rely on Redis replication.