When a queue isn't enough

Background job processing often starts with simple queuing systems like BullMQ, Celery, or Sidekiq, which are well-suited for atomic, self-contained tasks such as sending emails or processing files. However, as job complexity grows — requiring multi-step processes, indefinite waits for external signals, or handling retries without redoing costly operations — durable execution platforms like Temporal, Inngest, or AWS Step Functions become more appropriate. These platforms guarantee task completion by persisting execution state and enabling jobs to resume from the point of failure, offering inherent observability and flow control. For example, in a complex AI lead processing pipeline involving multiple APIs and human approval, a durable execution platform efficiently manages retries, state, and long-running tasks without the need for additional infrastructure to track progress or coordinate steps. While the rise of AI necessitates more durable solutions due to longer, stateful, and human-in-the-loop processes, simpler, atomic tasks can still be effectively managed by traditional queues if they can restart safely from the beginning.

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