Celery
Celery: A mature self-hosted queue for Python teams that need distributed task control. Ranked #7 of 36 in Task Queue Software by our editors (8.6/10); pricing: Open source; best for python teams needing mature distributed tasks.
At a glance
- Editor score8.6 / 10
- PricingOpen source
- Best forPython teams needing mature distributed tasks
- Paid fromFree
- Deployment modelSelf_hosted
- Facts checked20 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.
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Celery pricing
Celery fact sheet
| Free plan | Not verified |
|---|---|
| Paid from | Free |
| 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
Alternatives to Celery
- HatchetA code-first queue for durable execution, observable workflows, and AI task orchestration.9.4
- Trigger.devA strong fit for durable TypeScript tasks, queues, retries, and preview environments.9.2
- InngestA flexible task engine for serverless teams that need durable, observable execution.9.0
Celery vs the competition
- Celery vs Hatchet
- Celery vs Trigger.dev
- Celery vs Inngest
- Celery vs BullMQ
- Celery vs Taskiq
- Celery vs pg-boss
Compare Celery with any tool side by side →
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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