Cut LLM Costs

The Problem with LLM Bills

Your AI feature is a hit, but the OpenAI bill is scaling faster than your revenue. This happens when prototypes are pushed straight to production without architectural cost control.

Symptoms You'll Recognise

• Surge in tier-1 model costs (like GPT-4o) for simple classification tasks.
• Token waste from sending identical bulky contexts with every request.
• Users making the same generic queries that cost you money every single time.

Why It Happens

MVP architecture is about speed to market, often relying on the biggest model for every task because it "just works." When volume hits, the lack of a proper routing and caching middleware creates massive waste.

How We Fix It

1. Semantic Caching: We integrate a caching layer that stores embeddings of previous answers. If a user asks a conceptually identical question, it hits the cache (0 cost) rather than the LLM.
2. Dynamic Model Routing: We deploy an intelligent router that sends simple tasks (extraction, formatting) to 1B-8B parameter models (Llama-3, GPT-4o-mini) and reserves the expensive models only for complex reasoning.
3. Prompt Minification: We systematically strip unnecessary tokens from your system prompts without degrading performance.

Proof

Reduced inference costs by 68% for a legal-tech startup while maintaining 99% output parity, saving them $14k monthly.