A transit authority wants overnight ridership analytics summarized and cares more about cost than sub-second answers. What should developers prioritize?

Transit overnight ridership analytics are latency-tolerant summaries where cost usually outweighs sub-second interactivity. Matching cost-over-latency priorities to a batch-friendly Claude processing approach is correct because requirements analysis includes selecting the processing path that fits business priorities: overnight batch jobs can queue, run asynchronously, and optimize for price/throughput rather than chat SLAs. Conceptually this keeps municipal analytics budgets sustainable while still delivering morning-ready digests for planners.

Routing overnight summaries through streaming responses for incremental token delivery fails by concept: streaming exists to make a live session feel responsive token-by-token for a human watching the screen, which is a UX optimization, not a cost lever for an unattended overnight batch job nobody is staring at. Prioritizing sub-second response tuning for overnight batch summarization workloads fails as prioritization—it contradicts the stem's stated preference by chasing latency the authority explicitly said it does not need, and sub-second tuning typically raises cost rather than lowering it. Selecting a larger Claude model to enrich summary detail for overnight report batches fails the stem's cost framing: a bigger model raises per-token cost for extra detail the transit authority never asked for, when the lever to pull is the processing path, not the model size.

Exam caveat: CCDV-F often pairs 'cost over latency' with batch-friendly approaches; do not default to realtime prestige or oversized models when the stem cares about cost. Keep transit ridership overnight framing. Operational check: classify the job as latency-tolerant, select batch/async Claude processing, track cost per report set, and verify morning summaries meet quality bars without streaming or oversized-model patterns.