llm-cost-optimization

Techniques for cutting the real cost of running large language models in production, from token accounting to architecture decisions. This covers instrumenting where spend actually goes, such as tool schemas, context bloat, and per-turn overhead, then applying fixes like lazy-loading, caching, and prompt trimming. You will also find routing strategies that match each task to the cheapest capable model, plus the tradeoffs between frontier APIs, small specialized models, and local inference.

Iceberg with tiny green tip above water and huge submerged mass, symbolizing hidden AI coder token overhead.

ai-coder, token-cost, llm-cost-optimization, ai-coding-tools, developer-tools

Do You Know What You're Paying For When You Use an AI Coder?

Most of what you pay an AI coder per turn goes to overhead, not actual work. Real instrumentation shows tool schemas alone consume 68% of per-turn costs, and straightforward fixes like lazy-loading cut total session spend by 47%.

Sunlit home office with a glowing GPU workstation running local AI on a wooden desk.

local-ai-inference, small-language-models, llm-cost-optimization, diffusion-language-models, ai-infrastructure

The AI Golden Age Has Passed, Welcome to the AI Golden Age

The first AI golden age was built on renting frontier model compute by the token. The second is built on local inference, small specialized models, and routing architecture that puts the right task on the cheapest capable model.