Cost optimisation in AI projects: managing API and infrastructure spend

In enterprise-scale AI integrations, compute and token consumption costs can get away from you very quickly once systems are live. In high-traffic applications especially, every API call and every word processed carries a cost to the business. That is why budget limits have to be set clearly while the infrastructure is still being designed, and the architecture optimised against that budget.

The most effective way to bring costs down in a RAG system is to apply semantic caching and chunking strategies that cut the cost of unnecessary retrieval. The real engineering achievement is delivering that optimisation across infrastructure and token spend while never compromising on answer quality, speed, or the 99.9% availability target.