届时，问题已不再仅仅是选择哪个模型，或哪家提供商提供最低的令牌价格：而是如何以经济、可预测且可持续的规模运行人工智能。

人工智能正从孤立的试点项目转向生产组合：包括助手、检索与知识系统以及智能体应用。客户服务、IT、研究和业务流程智能体可以在企业系统中执行多步工作流，从而在模型、数据和工具方面产生持续的需求。

这种情况已经开始发生。德勤《2026年企业人工智能现状》反映了许多领导者所观察到的现象：员工对人工智能的访问率在2025年上升了5%，预计六个月内，至少有40%的人工智能项目投入生产的公司比例将翻倍。

当人工智能成为一组始终在线的工作负载，而非一系列实验时，经济逻辑就会发生变化。按用量计费为团队提供了灵活性并限制了承诺。但当使用量变得稳定、可预测且足够大以维持产能高效运行时，领导者需要提出一个不同的问题：继续每次请求购买人工智能在经济上仍然合理吗？还是时候投资于可以优化和控制的产能了？

这并非抽象的云与本地部署之争，而是针对每一项工作负载的业务决策。在未来12到18个月内，公司能合理预期多少人工智能需求？这些产能将被多一致地使用？当多个工作负载共享基础设施时，企业可以将固定成本分摊到更多的 productive 用途上——从而改善拥有成本的经济性。

关键在于你的运行量有多大

拥有产能并不自动意味着更低成本的答案。只有当企业能够保持产能高效运行时，它才具有合理性。

每个组织都有一个临界点，即持续使用水平达到该点时，拥有产能可能比每次请求购买更具经济性。没有通用的数字。这取决于所使用的模型、输入和输出令牌的平衡、性能要求、系统设计、能源成本以及支持所需的运营模式。

一个以检索为主的知识系统与一个简单的助手相比，其成本结构可能截然不同，因为它可能为每次交互处理更多的上下文。智能体工作流则可能有所不同：单个业务任务可能涉及重复推理、检索、模型调用和工具使用。这就是为什么通用的成本基准是不够的。企业需要对其实际工作负载进行建模，理解预期需求，并据此确定产能规模。

At that point, the question is no longer simply which model to consume, or which provider offers the lowest token price: It is how to run AI economically, predictably, and at sustained scale.

AI is moving from isolated pilots into production portfolios: assistants, retrieval-and-knowledge systems, and agentic applications. Customer-service, IT, research, and business-process agents can execute multi-step workflows across enterprise systems, creating recurring demand across models, data, and tools.

This is already starting to happen. Deloitte’s 2026 State of AI in the Enterprise reflects what many leaders are seeing: worker access to AI rose 5% in 2025, and the share of companies with at least 40% of their AI projects in production is expected to double within six months.

When AI becomes a portfolio of always-on workloads, not a collection of experiments, the economics change. Consumption pricing gives teams flexibility and limits commitment. But when usage becomes steady, predictable, and large enough to keep capacity productive, leaders need to ask a different question: Does it still make economic sense to buy AI one request at a time, or is it time to invest in capacity they can optimize and control?

This is not an abstract cloud-versus-on-premises debate. It is a workload-by-workload business decision. Over the next 12 to 18 months, how much AI demand can the company reasonably expect? How consistently will that capacity be used? When multiple workloads share infrastructure, the enterprise can spread fixed costs across more productive use—improving the economics of ownership.

The question is how much you run

Ownership is not automatically the lower-cost answer. It only makes sense when an enterprise can keep capacity productive.

Every organization has a crossover point, the level of sustained use at which owning capacity can become more economical than buying it one request at a time. There is no universal number. It depends on the models being used, the balance of input and output tokens, performance requirements, system design, energy costs, and the operating model required to support it.

A retrieval-heavy knowledge system can have a very different cost profile from a simple assistant because it may process far more context for every interaction. Agentic workflows can be different again: a single business task may involve repeated reasoning, retrieval, model calls, and tool use. That is why generic cost benchmarks are not enough. Enterprises need to model their actual workloads, understand expected demand, and size capacity accordingly.