Meridian Line's port-operations copilot endpoint is serving far more expensively than expected, and profiling shows the chain issues three separate Vector Search retrieval calls per user query, most returning overlapping results, before a single LLM call composes the answer. What is the most direct way to reduce cost here?

The profiling data points squarely at the chain's design, three retrieval calls per query returning overlapping results, as the source of the redundant cost, so the most direct fix is redesigning the chain to issue one well-formed retrieval call instead of three, which addresses the actual waste rather than optimizing something downstream of it. Provisioned throughput is not universally cheaper than pay-per-token, it becomes cost-effective at sustained high volume where dedicated capacity is fully utilized, and switching serving modes does nothing to reduce the number of retrieval calls the chain is issuing, it would not fix the described problem even if it lowered per-token cost in isolation. Reducing max_tokens on the final LLM call only affects the cost of that one generation step and ignores that the profiling identified the retrieval calls, not the LLM call, as the driver of the redundant cost, this targets the wrong part of the pipeline. Scale-to-zero controls whether idle capacity is released between periods of no traffic, it has no effect on the cost incurred per query while the endpoint is actively serving three retrieval calls for every single user question. Exam framing: cost control starts with identifying which stage of the chain is actually driving the spend, here retrieval, not by defaulting to a generic serving-mode or generation-parameter change.