Token wholesale mode emerges, enterprise inference costs plummet
In November 2026, the AI industry ushered in a new wave of pricing model changes. Previously, when using large models, enterprises often paid per call or per token unit, resulting in high costs. Recently, several major model service providers have begun offering "token wholesale" plans, allowing enterprises to purchase large token packages in monthly or annual installments, with unit prices dropping to 30% or even lower than original pay-as-you-go rates. A leading cloud vendor, in its "AI resource pool" released earlier this month, made token wholesale a flagship product; enterprise users can purchase packages ranging from hundreds of billions to trillions of tokens based on their business peak estimates, with a validity period of up to one year. This approach enables enterprises to support large-scale production applications with the "cheapest tokens," especially suitable for high-concurrency scenarios such as customer service, content moderation, and code generation.
At the same time, several AI startups have followed suit by launching elastic token pools that support quarterly or project-based billing. Analysts point out that token wholesale is a critical step toward the maturity of model-as-a-service (MaaS)—it shifts computing resources from "retail" to "wholesale," helping enterprises purchase intelligence costs like water and electricity. An AI lead from the financial sector noted that the new plan reduced their monthly model invocation budget by about 60%, while throughput increased by 50%. However, token wholesale is currently limited mostly to general dialogue and text generation models; for multimodal or high-precision reasoning models, customized contracts are still required.
AI transfer stations become a new channel for model distribution
Alongside the proliferation of token wholesale, the concept of "AI 中转站" quickly entered the enterprise's field of vision. The so-called AI transfer station is essentially a standardized API gateway that aggregates interfaces from multiple model providers and provides unified authentication, billing, and traffic scheduling capabilities. After integration, enterprises can invoke various engines including GPT-level, Claude-level, and multiple domestic open-source models with just one connection, and can dynamically select the best model based on real-time pricing and latency. A startup focused on enterprise AI infrastructure officially launched its "AI API Transfer Station" platform in mid-month, emphasizing no-code integration and intelligent routing. The platform claims to have integrated more than 20 mainstream models and built in load- and cost-based scheduling strategies; users only need to set a budget cap and latency tolerance, and the system automatically selects the "cheapest token" combination to complete tasks.
Unlike traditional single-model APIs, the AI transfer station also provides token-level usage monitoring and cost insights, allowing enterprises to clearly see which model and which step each request consumed. Within the first week of launch, the platform attracted over 200 enterprise registrations for trial. Industry observers believe that the AI transfer station essentially plays the role of an "AI channel agent," allowing model capabilities to flow between different suppliers like commodities, greatly reducing enterprise model switching costs and vendor lock-in risks. Notably, several cloud vendors are also internally testing similar "token transfer station" features, which may be launched as value-added services in the future.
Edge-side models and cloud-edge collaboration go live, AI hub ecosystem begins to take shape
In addition to cloud inference cost optimization, November 2026 also saw the emergence of multiple lightweight models for the edge side. A well-known AI research institute open-sourced a multimodal model with fewer than 3B parameters at the beginning of the month, specifically designed for mobile phones and IoT devices. Using mixed-precision quantization and sparse activation techniques, it maintains over 80% of the performance of large cloud models while reducing inference power consumption by more than 90%. The model attracted widespread attention from embedded developers upon release, with some manufacturers adapting it to their smart home control panels overnight.
Meanwhile, a developer community called "AI hub" launched a model adaptation marketplace at the end of the month, encouraging third-party vendors to upload edge-side models bound to specific hardware and providing unified testing and evaluation benchmarks. The emergence of AI hub allows developers to obtain validated AI model packages like downloading apps, significantly reducing the repeated development cost of edge applications. To further cover the needs of small and medium-sized enterprises, some service providers have begun offering "AI large model agency" services: enterprises only need to describe their business scenario, and the agency handles the entire process of model selection, cloud deployment, edge distillation, and token procurement. This one-stop service, combined with token wholesale and AI transfer stations, forms a closed loop from model supply to deployment and operations.
Industry experts predict that with the improvement of edge-side model capabilities and the continued decline in cloud token costs, 2027 will become the first year for large-scale adoption of AI applications. By then, enterprises will no longer need to build large AI teams; instead, through token wholesale, AI transfer stations, and agency services, they will be able to invoke top-tier AI capabilities with the low threshold of using water and electricity.