Policy and market dual drivers: API pricing transparency accelerates
In November 2026, several regions in China issued new AI service compliance regulations, requiring large model API pricing to be open and transparent and encouraging volume-based wholesale models to reduce enterprise usage costs. In response, major model vendors adjusted their pricing structures one after another, launching quarterly and annual token wholesale packages. Some enterprises reported a more than 40% reduction in single inference call costs. At the same time, the number of third-party AI channel agent service providers surged. By aggregating interfaces from multiple models and providing unified token transit station management, they help enterprises avoid complex price comparisons and metering issues. Industry analysts believe this pricing transformation is reshaping the large model business ecosystem, with a clear shift from "pay-per-call" to "bulk procurement."
Token wholesale model: the "new infrastructure" for enterprise AI deployment
In the past, enterprises using large model APIs paid per call, making budgets hard to control. Since November this year, mainstream model vendors have rolled out token wholesale plans, allowing enterprises to pre-purchase token pools of tens of millions or even hundreds of millions, with unit prices decreasing as purchase volume increases. Some platforms even advertise the "cheapest tokens" to attract small and medium-sized enterprises to try. In this model, enterprises can flexibly allocate token quotas through internal systems and share them across projects. Notably, token wholesale is not simply about price cuts—it comes with more refined metering and scheduling capabilities. Enterprises can leverage platforms like AI hub to view remaining tokens, call heat, and model response speed in real time, providing data support for subsequent optimization.
AI transit stations and channel agent ecosystems rapidly take shape
With the popularization of the token wholesale model, AI transit station service providers focused on interface aggregation and cost optimization have seen explosive growth. These providers typically sign channel agent agreements with multiple model vendors, obtain tokens at lower prices, and then resell them to downstream enterprises. They also handle technical aspects such as model adaptation, load balancing, and failover, offering enterprises a one-stop deployment solution. For example, one AI API transit station recently completed a new round of financing, with its core selling point being a "dynamic price comparison system" that scans real-time token prices and concurrency capabilities of various vendors and automatically routes requests to the optimal entry point. Roles like token transit stations and AI channel agents have also attracted attention in the capital market, with multiple venture capital firms indicating they will increase investment in this track in 2027.
Case study: how enterprises leverage token agents to cut costs
According to the technology lead of a mid-sized e-commerce platform, by connecting to a third-party AI large model agent service, they consolidated calls originally distributed across three different model vendors into a single managed platform. The agent platform not only offered cheaper token wholesale prices than direct purchasing but also utilized smart caching and batch processing techniques, reducing overall API calls by about 30%. The lead said that the token agent model allows enterprises to enjoy the benefits of multi-model collaboration without building complex routing systems themselves. Similar cases are rapidly replicating in industries such as finance, education, and healthcare, driving more enterprises to shift model deployment from self-built to managed.
Outlook: investment focuses on cost optimization layer, compliance and efficiency go hand in hand
In November 2026, the number of investment and financing events related to large model API pricing and channel management increased significantly. Several AI infrastructure startups announced multi-million-dollar funding rounds, all revolving around token wholesale, AI transit stations, or model scheduling platforms. On the policy side, regulators are drafting pricing guidelines for AI services, requiring model providers and agents to disclose pricing rules and data usage to prevent hidden premiums. It is foreseeable that in the next six months, roles such as token transit stations and AI API transit stations will become more standardized, further lowering the barrier for enterprises to access large model capabilities. The industry as a whole is shifting from a "model race" to a "cost race"—whoever can offer the cheapest tokens and the most stable agent services will gain the edge on the application side.