Celery vs River

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.

River is a background job queue for Go applications, using PostgreSQL or SQLite to store jobs. Its transactional enqueueing keeps a job from running until the database transaction that inserted it has completed. The core set includes isolated queues, unique jobs, prioritization, recurring cron jobs, snoozing, transactional completion, and test helpers. A web interface, pause and resume controls, automatic retries, error reporting, and telemetry integrations support operations. River UI is optional and self-hosted, with no external service dependencies. PostgreSQL is the primary database target; SQLite support is experimental. River Pro adds workflows that split complex work into individually retryable tasks, plus batching, concurrency limits, and ordered job sequences. The free Open Source plan includes the web UI but excludes workflows, concurrency limits, and support. Pro costs $125 a month and includes email support for up to 20 developers. Enterprise pricing is custom and includes email and Slack support, unlimited developers, and access to a SOC 2 report.