LLM Serving Cost and Latency Tradeoffs

Serving a large language model in production — for example, summarizing earnings call transcripts or classifying news headlines for a trading signal — costs money and time for every request, and those two costs pull in opposite directions. A bigger, more capable model tends to produce better answers but is slower and more expensive per query; a smaller or more heavily compressed model responds faster and cheaper but with lower accuracy. Firms have to pick a point on this tradeoff based on what the application actually needs.

The choice matters differently depending on use case. A real-time trading signal that needs to react to breaking news within seconds cannot tolerate a slow model, even a highly accurate one, because a correct answer that arrives too late is worthless. An overnight research summary of hundreds of filings, by contrast, can use a larger, slower, more accurate model since there's no urgency, and the cost per query matters more when running at that scale.

Worked example. A firm needs to classify the sentiment of thousands of news headlines per minute for a trading signal. Using its most accurate large model would cost $0.02 per headline and take 800 milliseconds to respond — too slow and too expensive at that volume. Switching to a smaller, distilled version of the same model drops the cost to $0.001 per headline and the latency to 50 milliseconds, at a small accuracy cost the firm measures and decides is acceptable given how much faster the signal can now act on breaking news.