随着人工智能从试验项目逐步进入生产阶段，企业正面临关键的经济决策。1根据德勤2026年《企业AI现状》报告，2025年员工访问AI的比例上升5%，预计未来6个月内AI项目投入生产的公司比例将翻倍。1在这一转变过程中，企业需要重新评估采用按需消费模式还是投资自有容量的问题。1
当AI工作负载从零散试点演变为持续生产投资组合时，拥有专属基础设施相比按单次请求付费可能更具经济效益。1然而，这一切的前提是企业能够通过完善的运营模式保持容量的生产效率。1由于不同企业使用的模型、输入输出令牌平衡、性能要求、系统设计、能源成本和运营方式各不相同，并不存在通用的成本转折点数字。1
企业领导者需要考虑三个关键问题：需求是否变得稳定、可预测且足够大？在什么使用水平下拥有基础设施才具经济意义？能否通过采用、治理和持续用例扩展来保持容量的生产效率？1未来12至18个月是企业做出工作负载成本决策的关键期。1
As artificial intelligence projects advance from experimental pilots to sustained production environments, companies must reassess their infrastructure investment strategies, according to analysis from MIT Technology Review.1 The shift from scattered proof-of-concept initiatives to continuous operational AI portfolios requires organizations to evaluate whether on-demand consumption pricing or dedicated capacity ownership delivers better economics.1
A 2026 State of Enterprise AI report from Deloitte indicates that employee access to AI tools will rise by 5 percent in 2025, with the proportion of companies deploying AI projects into production expected to double within the next six months.1 However, determining the optimal financial transition point between pay-per-use and owned infrastructure depends on multiple variables without a universal threshold.1 The choice hinges on the specific models deployed, the balance of input and output tokens, performance requirements, system architecture, energy costs, and operational practices.1
Enterprise leaders must address three foundational questions to guide their investment decisions: whether demand has stabilized into predictable and sufficiently large volumes, at what usage level owning infrastructure becomes economically justified, and whether organizations can sustain the production efficiency of their capacity through adoption governance and continuous case expansion.1 The next 12 to 18 months represent a critical window for companies to make these workload cost decisions.1
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