The Hidden Cost of Context Compression in Cloud AI APIs

Mnemara, a context management tool designed for local models with limited windows, creates counterproductive effects when applied to cloud-based AI like Claude. While Mnemara successfully reduces tokens through aggressive context eviction, cloud APIs rely on prompt caching—sending identical prefixes repeatedly to achieve 90% cost reduction. Each time Mnemara modifies context, it breaks the cache, negating savings and billing the next call at full price. This inversion reveals a fundamental difference: local models are constrained by compute, cloud models by economics.