From Thousands to One: The LLM Pattern for Scaled AI Security

As organizations scale their LLM-powered features, making individual API calls per user quickly becomes cost-prohibitive and creates rate-limiting bottlenecks—a problem CoinHawk solved elegantly. The single-call pattern they demonstrate, where a shared LLM request refreshes on a timer and serves cached results to all users, fundamentally changes how companies should architect expensive global compute operations. This approach extends beyond security monitoring to status pages, anomaly detection, and market summaries, making it a critical architectural pattern for any production AI application.