Evaluating the lifecycle economics of AI: The levelized cost of artificial intelligence (LCOAI)

Abstract

As artificial intelligence (AI) becomes foundational to enterprise infrastructure, organizations face growing challenges in accurately assessing the full economic implications of AI deployment. Existing metrics such as API token costs, GPU-hour billing, or Total Cost of Ownership (TCO) fail to capture the complete lifecycle costs of AI systems and provide limited comparability across deployment models. This paper introduces the Levelized Cost of Artificial Intelligence (LCOAI), a standardized economic metric designed to quantify the total capital (CAPEX) and operational (OPEX) expenditures per unit of productive AI output, normalized by valid inference volume. Analogous to established metrics like the Levelized Cost of Electricity (LCOE) and the Levelized Cost of Hydrogen (LCOH) in the energy sector, LCOAI provides a rigorous, transparent framework for evaluating and comparing AI deployment strategies. We define the LCOAI methodology in detail and apply it to four representative scenarios OpenAI GPT-4.1 API, Anthropic Claude Haiku API, a self-hosted LLaMA-2–13B deployment, and a cloud-hosted LLaMA-2–13B deployment demonstrating how LCOAI captures critical trade-offs in scalability, investment planning, and cost optimization. Extensive sensitivity analyses further explore the impact of inference volume, CAPEX, and OPEX variability on lifecycle economics. The results illustrate the practical utility of LCOAI in procurement, infrastructure planning, and automation strategy, and establish it as a foundational benchmark for AI economic analysis. Policy implications and directions for future refinement, including integration of environmental and performance-adjusted cost metrics, are also discussed.