How We Cut LLM Inference Costs by 60%

A logistics client was spending $48,000 per month on LLM inference. After six weeks of optimization, they were at $19,200 with measurably better quality on three of their five use cases.

Technique 1: Route by Complexity

Not every query needs GPT-4o. We built a lightweight classifier that routes simple, structured queries to a smaller model and reserves the frontier model for complex reasoning. 68% of queries went to the cheaper model.

Savings: ~$14,000/month

Technique 2: Aggressive Prompt Compression

System prompts had grown organically to 2,400 tokens. After stripping redundant instructions and compressing examples, the effective system prompt was 780 tokens — with identical output quality on our evaluation suite.

Savings: ~$4,200/month

Technique 3: Semantic Caching

Many user queries are near-duplicates. We built a semantic cache: embed the query, check if a similar query (cosine similarity > 0.94) has been answered recently, return the cached response. Cache hit rate: 31%.

Savings: ~$6,800/month

Technique 4: Batch Non-Urgent Workloads

Daily report generation, document summarization, and similar batch tasks ran during peak hours at full price. Moving them to the Batch API reduced cost by 50% for 22% of total token volume.

Savings: ~$2,100/month

Technique 5: Output Length Control

The model was generating 600-token responses for queries that needed 150 tokens. Adding explicit length constraints in the system prompt reduced output tokens by 55% on average.

Savings: ~$3,700/month

Lessons Learned

Cost optimization and quality improvement are not in tension. Routing simple queries to simpler models is not a quality compromise — it is matching the tool to the task.