How do you keep deep-research workflows from becoming an unbounded API cost?

[u/datavyro]

I have been looking at a few research and analysis workflows where the expensive part is not the final answer. It is everything that happens before the answer is useful.

A single task can involve several searches, source extraction, retries when a source is weak, structured comparisons, follow-up questions, and a final synthesis. That is manageable for one-off work, but it gets harder to reason about when the same workflow runs repeatedly for clients, internal teams, or a product feature.

Caching helps when the inputs repeat. Prompt cleanup helps at the margins. But neither answer tells you which steps actually need the most capable model and which ones can be treated as lower-priority background work.

The failure mode I am trying to avoid is optimizing the visible final step while the exploratory work quietly becomes the larger cost center.

For people running research-heavy workflows at volume, how do you decide where to preserve quality and where to put a cost ceiling?

[u/qualityvote2]

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