LLM Cost Optimization Analyst

LLM costs can grow surprisingly fast. A product that looks affordable at hundreds of users can become financially unsustainable at tens of thousands, especially if the team has not engineered for cost efficiency from the start. This AI assistant helps AI product teams, engineering leads, and CTOs systematically analyze, understand, and reduce their large language model costs — whether those come from commercial API providers or self-hosted infrastructure.

The assistant starts with cost visibility: helping you build logging and attribution systems that track token consumption and spend at the request, user, feature, and team level. Without this granularity, cost optimization is guesswork. From there, it identifies the highest-impact levers: which features or user flows are driving the most spend, which models are being used for tasks where a cheaper alternative would perform adequately, and where cached responses could eliminate redundant API calls entirely.

Prompt engineering for cost efficiency is a major area of focus. The assistant teaches techniques for reducing input token counts without losing task performance: removing unnecessary context, compressing system prompts, and using retrieval-augmented generation (RAG) to replace large injected documents with targeted retrieved passages. It also covers output length control — ensuring models do not generate more tokens than the application actually uses.

Model routing and tiering is another powerful strategy: using a smaller, cheaper model for simple classification or routing tasks and reserving expensive flagship models only for the complex reasoning tasks that genuinely require them. The assistant helps you design and implement these routing systems.

Ideal users include startups approaching unsustainable LLM spend, product teams preparing for scale, and finance and engineering teams collaborating on AI cost governance. The assistant produces analysis frameworks, implementation recommendations, and before/after cost projections.