LLM Cost Optimization: Cutting Spend Without Cutting Quality
Most teams overpay for LLM inference by 40-60%. Model routing, prompt compression, and caching are the levers that actually move the needle.
LLM costs scale linearly with usage, and usage grows faster than anyone plans for. The first instinct is to switch to a cheaper model, but that often degrades quality. The real savings come from routing, caching, and compression — reducing the number of tokens that reach the expensive model in the first place.
Model routing: the right model for the task
Not every request needs a frontier model. Classification, formatting, and extraction can run on a small, cheap model. Complex reasoning and tool use need the frontier model. A router that classifies the request and sends it to the appropriate tier can cut cost by 50%+ with negligible quality loss. The router itself can be a small model or a rules engine.
Semantic caching
If two users ask the same question, you pay twice. A semantic cache — keying on the embedding of the prompt, not the exact string — catches near-duplicate queries and returns the cached response. Hit rates of 20-40% are common in support and FAQ use cases. The cache must be invalidated when the underlying data changes, or it serves stale answers.
Prompt compression
Long prompts are long because of preamble, repeated examples, and context that could be summarized. Compressing the system prompt — removing redundancy, merging overlapping instructions, shortening examples — reduces input tokens on every call. A 2000-token system prompt compressed to 1200 saves 40% on every request, forever.
The metric: cost per successful outcome
Do not optimize cost per token. Optimize cost per successful outcome. A cheaper model that fails 30% of the time and requires retries may cost more per success than the expensive model that succeeds first try.
