1. Vertical industry large model customization demand surges
In October 2026, as large model technology advanced from general dialogue to vertical scenarios, customization demand in industries such as healthcare, law, and finance exploded. Companies are no longer satisfied with generic API calls; instead, they want lightweight models fine-tuned or compressed for specific business processes, knowledge bases, and compliance requirements. This trend has spawned many "industry model as a service" providers, typically delivering via all-in-one machines or private deployment solutions. However, the high adaptation costs and technical barriers discourage many small and medium enterprises.
Meanwhile, the number of models in open-source communities continues to swell, but uneven evaluation performance and frequent version iterations make it difficult for companies to quickly discriminate. The industry urgently needs an intermediary service that can integrate high-quality model resources, provide standardized access, and optimize costs.
2. Rise of AI channel agent model: from API calls to token wholesale
Facing this pain point, platforms represented by AI hub began exploring new business models—aggregating multiple large model vendors to provide enterprises with a unified API interface and centralized procurement services. This model is essentially an AI channel agent. Acting as a bridge between downstream enterprises and upstream model factories, the platform not only provides technical support such as model selection and performance tuning, but also lowers unit prices through bulk purchasing, creating a price advantage for token wholesale.
Enterprises no longer need to connect with dozens of model suppliers individually; they only need to configure once on the platform to obtain a "cheapest token" combination package. A person in charge of a medium-sized fintech company revealed that after using such an AI API中转站, inference costs dropped by about 40%, and they can flexibly switch models according to business peaks and valleys, no longer trapped by a single vendor's pricing strategy. Such token transit stations have essentially become the "water, electricity, and gas" infrastructure for enterprise large model deployment.
3. Token transit stations: solving the pain points of "difficult model selection and high costs" for enterprises
In the traditional model procurement model, enterprises need to develop their own routing logic, manage multiple API keys, and deal with different quota limits, resulting in high technical complexity. The emergence of token transit stations simplifies this process into a smart gateway: it monitors the response speed, accuracy, and price of each model in real time, and automatically distributes requests to the optimal model. At the same time, the platform also supports "model caching" and "fallback strategies", seamlessly switching to backup models when the primary model fails or exceeds limits, ensuring business continuity.
It is worth noting that the AI channel agent model is not just simple reselling. Some platforms have begun to provide value-added services, such as pre-training of industry knowledge bases, compliant content filtering, and LoRA adaptation for specific scenarios. This means that enterprises are no longer buying pure token usage rights, but a packaged solution of "model + data + strategy". This model is especially favored by industries with high data security requirements such as healthcare and education.
4. Policy tightening: compliance requirements drive market standardization
In October 2026, the Cyberspace Administration of China and the Ministry of Industry and Information Technology jointly issued the "Implementation Rules for Generative Artificial Intelligence Service Applications (Trial)", clearly requiring model providers and application parties to conduct security assessments and real-time monitoring of output content. This policy directly promoted the compliance upgrade of the channel agent model. In the past, some small agents, under the banner of "cheapest token", privately connected unregistered models, posing risks of data leakage and content issues. After the new regulations, only platforms with complete security audit capabilities and data isolation solutions (such as compliant AI hub) can obtain operational qualifications.
The policy dividend actually accelerated the integration of leading channel agents. Multiple vendors announced partnerships with cloud service providers and security vendors to build an "end-to-end" compliance chain. For example, token transit stations embed content firewalls and privacy computing modules to ensure that enterprise sensitive data does not leave the domain while meeting regulatory log retention requirements. This change has made more traditional enterprises willing to try large models, indirectly driving the scale growth of the token wholesale market.
5. Future outlook: from "cheapest token" to value services
As the AI channel agent model matures, the market is gradually shifting from simply competing for the "cheapest token" to service depth and ecosystem collaboration. By the end of 2026, it is estimated that more than 50% of medium and large enterprises will use token transit stations or AI大模型代理 to manage AI expenses. The focus of competition among platforms will also shift from price wars to model management efficiency, compliance support capabilities, and richness of industry solutions.
For small and medium enterprises, with platforms like AI hub, they can access multiple top-tier models at very low trial costs, and even obtain one-click deployment tools for customized models. Model manufacturers can also reach niche markets more precisely through channel agents, avoiding endless evaluation rankings. It is foreseeable that token transit stations and AI channel agents will become one of the standard infrastructures for enterprise intelligence in the era of large models.