LLM Cost Control FAQ for Enterprise Teams
aiengineering
Answers to the cost questions engineering leaders ask when GPU bills climb: caching ROI, quantization risk, routing tradeoffs, and where budgets actually leak.
Cost questions come up on every LLM program review. These are the ones we answer most often.
Q: Where does most of our token spend actually go?
Usually three places: long system prompts replayed on every call, agent loops that re-read the same context, and retrieval pipelines that stuff top-20 chunks when top-5 would do. Instrument cost per user journey before optimizing model choice — the model is rarely the biggest lever.
Q: Is prompt caching safe to turn on globally?
Yes. Prompt caching is deterministic: the same prefix produces the same KV state. Turn it on, structure prompts with the stable content first (system, tools, few-shot), and put user input last. Expect 30–70% cost reduction on repetitive workloads.
Q: What about semantic caching?
Semantic caching lifts hit rate but can serve stale or subtly wrong answers when embeddings collide. Use it only where a wrong hit is cheap to detect, and always log a sample for human review.
Q: Should we quantize to 4-bit to cut cost?
Sometimes. Four-bit is fine for classification, extraction, and short-form generation, but collapses on multi-step reasoning and long chain-of-thought. Benchmark on your own eval set, not vendor charts.
Q: Does model routing actually save money?
Yes, when you have a real traffic distribution. Route easy classifications to a small model, escalate to a larger one on low confidence or complex intents. A 70/30 split can halve costs with negligible quality loss.
Q: What's the fastest cost win most teams miss?
Shorter, structured outputs. Capping max_tokens and forcing JSON schemas cuts decode time — the memory-bandwidth-bound half of inference — more than any model swap.
For a full governance view of cost, quality, and risk, see the Silverberry AI Platform.