Celery

Free#7 of 36 in Task Queue Software

Open-source distributed task queue for Python applications.

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At a glance

Editor score
Not yet scored
Pricing
Open source
Best for
Python teams needing mature distributed tasks
Paid from
None
Deployment model
Self_hosted
Facts checked
20 Sep 2026

Where it wins
- Runs tasks across worker processes and machines
- Flexible retries, scheduling, routing, and concurrency options
- Broad broker, backend, database, and search integrations
Where it doesn't
- Official SDK coverage is Python-only
- Teams manage workers, brokers, and result stores themselves
- Dead-letter handling depends on broker configuration

Our verdict on Celery

Celery is an open-source, self-hosted distributed task queue for Python applications. It lets applications submit asynchronous work through a message broker while workers execute tasks across processes or machines. The feature set suits Python teams that need more control than a simple in-process queue, including task routing, result tracking, worker events, scheduled jobs, and configurable retry behavior. Celery runs on Linux and macOS, with concurrency pools including prefork, gevent, eventlet, threads, and solo.

Its strongest fit is an environment with varied infrastructure requirements. Celery integrates with RabbitMQ, Redis, Amazon SQS, Kafka, and Google Cloud Pub/Sub for messaging, while result and data connections include SQLAlchemy, Django ORM, Elasticsearch, Cassandra, and MongoDB. Celery Beat supports periodic and cron-style scheduling, and task policies can include retry counts, delays, exponential backoff, and jitter. These options give teams room to shape execution around different workloads rather than adopting a single fixed queue model.

The trade-off is operational ownership. Celery is software rather than a hosted service, so teams manage their own workers, brokers, and result stores. Monitoring and worker-management events are available, but the overall deployment still calls for a team comfortable configuring distributed components. Dead-letter handling depends on broker configuration; rejected tasks may be moved to a dead-letter queue. Choose Celery when Python support, distributed execution, and configurable task behavior are priorities. A hosted service or a broader official SDK footprint may suit teams seeking less infrastructure responsibility or languages beyond Python.

Celery pricing

Plans Open sourceFree Free to use — no paid tier required for the core job.

See plans on docs.celeryq.dev

Celery fact sheet

Free plan	Not verified
Paid from	None
Deployment model	Self_hosted
Custom retry policy	Yes
Scheduled jobs	Yes
Dead-letter queue	Yes
Maximum payload size	Not verified
Official SDK languages	Python
Deployment	Self-hosted
Platforms	Linux, macOS
Support	Community, Docs
Built for	Small business, Mid-market, Enterprise (editorial estimate)
Integrations	10 integrations: RabbitMQ, Redis, Amazon SQS, Kafka, Google Cloud Pub/Sub, SQLAlchemy … See all →
Pricing	Open source
Website	docs.celeryq.dev
Facts checked	20 Sep 2026

Celery integrations

Celery lists 10 integrations on its own site.

RabbitMQ
Redis
Amazon SQS
Kafka
Google Cloud Pub/Sub
SQLAlchemy
Django ORM
Elasticsearch
Cassandra
MongoDB

See all Celery integrations →

Alternatives to Celery

See all Celery alternatives →

Celery vs the competition

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The editor score above is our own research. What this page doesn't have yet is a reader's view — what you used Celery for, what worked and what didn't. No stars are seeded and no review is paid for; an editor reads every one before it appears.

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Reviewed by iTechGuides Editors · Editorial team · Updated Sep 2026

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