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.