Celery is my standard tool for background processing, asynchronous workflows, and scheduled jobs in Python applications. It becomes important when a system needs to do meaningful work that should not block user-facing requests.
I use Celery for email sending, scheduled maintenance tasks, integrations, data processing, notification workflows, and any operation that is better handled outside the request-response cycle. That separation improves responsiveness and helps keep user-facing interactions fast and reliable.
More importantly, Celery helps clarify architectural boundaries. It encourages teams to decide what needs immediate execution and what should be delegated to a job system with retries, scheduling, and failure handling.
In production systems, that distinction matters. Celery supports cleaner backend behavior, better resilience, and a more scalable approach to operational workloads that would otherwise create bottlenecks.