How to Keep AI Costs Under Control

The rapid adoption of LLMs and managed AI services can lead to unexpectedly high and volatile costs, as spend is usage-based and can quickly escalate without proper oversight. To control AI spend, four main approaches are presented: using unified platforms for both AI and cloud costs, extending native cloud cost tools, optimizing GPU and Kubernetes efficiency, and implementing AI-specific governance layers. Unified platforms like Finout offer comprehensive visibility, while cloud-native tools are pragmatic for single-provider setups. For GPU-heavy workloads, infrastructure optimization tools are key, and API-aware governance layers help manage spend from multiple experimental teams.