Your team is generating nightly summaries of customer support tickets across thousands of tickets. The job runs overnight, and results are reviewed the next morning. Your manager suggests switching from real-time API calls to the Message Batches API to save 50% on costs.
The same team also runs a real-time pre-deploy check that must complete in under 60 seconds before code can be merged. The manager wants to switch this to Message Batches too.
What's the right call?
Why did you pick that answer? Two or three sentences. The act of articulating it is what builds the judgment — not the click that follows.
Message Batches offer 50% cost savings but with up to a 24-hour processing window and no guaranteed latency SLA. That makes them ideal for non-blocking, latency-tolerant workloads — overnight summaries are a textbook fit. They're inappropriate for blocking workflows where someone is waiting on the result, like a 60-second pre-deploy check. Match the API to the workload's latency tolerance: synchronous for blocking, batch for asynchronous.
"Most batches complete faster" isn't an acceptable guarantee for a workflow that must complete in 60 seconds. Tail latency on a batch could be hours. Optimistic averages are not a foundation for blocking workflows.
The cost difference is significant on workloads of any scale, and Message Batches aren't operationally complex for non-blocking jobs. Refusing to use them at all forfeits real savings on workloads where they're a perfect fit.
A timeout fallback adds complexity and doesn't actually save time on the affected runs — you've still waited 60 seconds before falling back, then need to make the real-time call. Net latency is worse than just using real-time from the start.