AI Cost Optimisation

This comprehensive guide tackles the financial realities of scaling AI systems in production environments. It reveals that AI cost problems stem primarily from architectural inefficiencies rather than model pricing alone. The piece breaks down six cost layers (model, token, retrieval, orchestration, latency, and failure costs) and presents four optimization pillars: context compression, model right-sizing, retrieval efficiency, and execution optimization.

The guide provides enterprise governance frameworks covering observability dashboards, cost accountability structures, and organizational rituals to maintain cost discipline. It targets mid-to-senior level product and engineering leaders already operating AI systems at meaningful scale, emphasizing that scaling from prototype to production reveals hidden cost multipliers requiring systematic redesign rather than tactical tweaks.