Procrastinate review
A focused open-source queue for Python teams standardizing on PostgreSQL.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Procrastinate is an open-source distributed task-processing library for Python applications. It stores task definitions, locks, and jobs in PostgreSQL, allowing teams to process work without a separate message broker. The library supports synchronous and asynchronous tasks, worker processes, queue selection, concurrency controls, job priorities, and task or queue locks. It is a strong fit for Python teams standardizing on PostgreSQL, particularly those building Django or PostgreSQL-based application environments. Teams seeking a broader integration ecosystem or a managed service should look elsewhere.
Its scheduling and retry controls are the main reasons to consider it. Jobs can run immediately, in the future, or periodically, while retry behavior can use configurable retry counts with constant, linear, or exponential backoff. Developers can manage workers, defer jobs, and handle schema management through the command-line interface. Custom JSON encoders and decoders add control over task data, and connectors for Django, SQLAlchemy, and ASGI support common Python application setups. This combination gives PostgreSQL-focused teams a focused task queue with useful execution controls rather than a broad, multi-platform integration layer.
The trade-off is a narrow operating model: deployment is self-hosted, PostgreSQL is central to storage and coordination, and the documented ecosystem is concentrated around Python and a small set of integrations. That makes Procrastinate a sensible choice when database alignment, scheduling, retries, and queue controls matter more than hosted operations or extensive platform coverage. Choose it for a Python application that already fits its PostgreSQL architecture; consider an alternative when your team needs a managed deployment, a wider integration footprint, or a queue that is not built around PostgreSQL.
Procrastinate pros and cons
- Where it wins
- Synchronous and asynchronous execution with queue and concurrency controls
- Immediate, future, and periodic scheduling with configurable retries
- Django integration plus CLI tooling for workers and schema management
- Where it doesn't
- Requires PostgreSQL-backed task and job storage
- Self-hosted deployment puts operations and maintenance on the team
- Integration footprint centers on Django, SQLAlchemy, and ASGI
Procrastinate fact sheet, pricing and score →
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