LLM Cost Calculator

Large Language Model (LLM) API costs are the single largest operational expense for companies running AI agents in production. As you scale from prototype to production, costs can grow 10-50x - often without proportional improvements in output quality.

LLM cost optimization is the practice of systematically reducing token usage and API spend without degrading response quality. The three primary techniques are:

Prompt compression

Most production prompts carry 25-40% overhead: verbose system instructions repeated on every call, excessive few-shot examples, and bloated retrieved context. Structured prompt compression extracts static instructions into compact formats, reduces examples to the minimum needed for consistent output, and trims retrieved context to the most relevant chunks.

Intelligent model routing

Not every task requires GPT-4-class reasoning. A model router classifies incoming requests by complexity and routes simple tasks (reformatting, extraction, classification) to cheaper, faster models while keeping complex multi-step reasoning on frontier models. This typically redirects 50-65% of calls to models that cost 10-20x less.

Semantic caching

Many agent queries are semantically identical even when worded differently. A semantic cache computes embeddings for incoming queries and serves cached responses for near-matches. Hit rates of 15-30% are typical, with some workloads reaching 40%+.

At Nexuron, we specialize in LLM cost optimization for teams spending $10K to $500K+ per month. Applied together, these techniques typically reduce spend by 30-50%, measured against your own eval suite. Book a free cost assessment to see what your savings potential looks like.