Why your AI pipeline costs 10x more after the demo
The New Stack Hafiz Hassan
AI application teams discover that production costs rise sharply because token-heavy architectures keep sending unnecessary text even when the model itself works as expected. The article cites prompt caching that can cut costs by up to 90% by reusing static prefix attention computations. As a result, developers are pushed to apply architectural token optimization (prompt caching, semantic caching, history summarization, retrieval/tool trimming, and routing) instead of only switching to cheaper models.
Why it matters
Every token has a price. The problem is that most AI systems don’t reveal the bill until they reach production. The post Why your AI pipeline costs 10x more after the demo appeared first on The New Stack.