Reducing LLM Inference Costs by 60%: A Case Study

When a B2B SaaS company came to us, their AI features were a success story — too successful. Their monthly LLM inference bill had ballooned to $42,000 and was growing 30% month-over-month. They needed to bring it down without degrading the user experience.

Here’s exactly how we did it.

Step 1: Instrument Everything First

Before optimizing anything, we added comprehensive logging to every LLM call: prompt token count, completion token count, model used, latency, and a hash of the prompt template. Within 48 hours, we had the data we needed.

The findings were striking:

• 34% of all API calls were near-identical requests within a 10-minute window
• 22% of prompts were vastly more verbose than they needed to be
• The company was using GPT-4 for tasks that GPT-3.5 handled just as well

Step 2: Implement Semantic Caching

The biggest win came from semantic caching using a vector store. Instead of hitting the LLM API for every request, we first computed an embedding of the incoming query and checked if a semantically similar question had been answered in the last hour.

For a FAQ-style support bot, this eliminated 41% of API calls entirely. The cache TTL was tuned per use-case — shorter for dynamic data, longer for stable product documentation.

Result: 41% reduction in API calls.

Step 3: Prompt Compression

We audited every system prompt in the codebase. The average system prompt was 847 tokens — many contained redundant instructions, repeated examples, and verbose formatting guides that could be compressed.

Using a combination of manual editing and LLMLingua (a prompt compression research technique), we reduced average prompt length by 38% with no measurable quality degradation across our evaluation suite.

Result: 38% fewer tokens per call.

Step 4: Model Routing

Not every task requires GPT-4. We built a lightweight classifier that analyzed incoming requests and routed simple, well-defined tasks (classification, extraction, formatting) to GPT-3.5-turbo, while reserving GPT-4 for complex reasoning tasks.

We built an evaluation harness with 500 representative examples to validate quality for each route before deploying.

Result: 55% of requests now served by the cheaper model.

The Combined Result

Metric	Before	After
Monthly API Cost	$42,000	$16,400
P95 Latency	3.2s	1.1s
Cache Hit Rate	0%	41%
Quality Score (eval)	87%	89%

The latency improvement was an unexpected bonus — cached responses are nearly instantaneous, and the smaller models are significantly faster.

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LLM cost optimization is engineering, not magic. If your AI infrastructure costs are growing faster than your revenue, let’s talk — we’ve done this before.