How We Cut LLM Costs by 47% Without Cutting Quality

A Series B FinTech company came to us spending $102K/month on LLM API calls across 8 production agents. Response quality was acceptable, but the burn rate was unsustainable. Within two weeks, we reduced that spend to $55K/month — a 47% reduction — while maintaining identical output quality on their internal eval suite.

Here is the exact playbook we used.

Step 1: Trace-Level Cost Attribution

Before optimizing, you need to know where the money goes. Most teams track total API spend but cannot attribute costs to individual agent runs, tools, or conversation turns. We instrumented every LLM call with trace-level cost tagging: model used, input tokens, output tokens, cache status, and the business workflow that triggered it.

The results were revealing. Three agents accounted for 78% of total spend. One internal summarization agent was making redundant calls on near-identical inputs — burning tokens on work already done minutes earlier.

Step 2: Prompt Compression

Most enterprise prompts carry significant overhead: verbose system instructions repeated on every call, excessive few-shot examples, and bloated context windows stuffed with retrieved documents that the model never references. We applied structured prompt compression across all 8 agents.

The technique involves extracting static instructions into a compact format, reducing few-shot examples to the minimum needed for consistent output, and trimming retrieved context to only the most relevant chunks. Average prompt length dropped by 34% with no measurable quality impact.

Step 3: Intelligent Model Routing

Not every task requires GPT-4-class reasoning. We implemented a model router that classifies incoming requests by complexity and routes them to the most cost-effective model. Simple lookups and reformatting tasks go to smaller, faster models. Complex multi-step reasoning stays on the frontier model.

The router itself runs on a lightweight classifier that adds less than 20ms of latency and costs fractions of a cent per call. The savings from routing 62% of calls to cheaper models more than offset the router cost.

Step 4: Semantic Caching

Many agent queries are semantically identical even if the exact wording differs. We deployed a semantic cache layer that computes embeddings for incoming queries and checks for near-matches against recent responses. Cache hit rates averaged 23% across all agents, with the internal summarization agent hitting 41%.

The cache uses a configurable similarity threshold — too low and you serve stale results, too high and you miss valid cache hits. We found 0.95 cosine similarity to be the sweet spot for most enterprise use cases.

Results

After two weeks of implementation and one week of monitoring, the combined savings were: prompt compression saved 19%, model routing saved 18%, and semantic caching saved 10%. Total monthly spend dropped from $102K to $55K — a $564K annualized saving.

The eval suite showed zero quality regression. In fact, response latency improved by 31% because cached and routed responses returned faster than frontier model calls.

When to Optimize

If your monthly LLM spend exceeds $10K and you have not done a cost audit, there is almost certainly 30-50% waste to reclaim. The techniques above are straightforward to implement and the ROI is immediate. Book a free strategy call and we will show you exactly where your spend is going.