Over the past two years, price cut announcements for LLM APIs have been almost nonstop. Many teams focus their attention on GPU purchase prices and complete machine quotes, believing that whoever gets cards cheaper can push inference prices down. But after tracking operational data from several major domestic computing hubs since last year, my judgment has changed: GPU purchase prices are becoming a one-time cost, and what truly creates a long-term gap is electricity costs.
Training is a one-time investment, while inference burns money every day. The compute consumed by a single inference request, multiplied by tens of millions of daily calls, makes electricity the heaviest component of API pricing. So in the second half of the price war, what matters is not who buys cards cheaper, but who can obtain cheap and stable electricity.
The electricity price gap between east and west is reshaping the inference cost structure
Domestic computing layout has a very obvious characteristic: computing centers are moving west, while businesses and users remain concentrated in the east. Places like Inner Mongolia, Ningxia, and Gansu have good wind and solar resources, and industrial electricity prices are significantly lower than along the eastern coast. The comprehensive electricity cost obtained by data center operators can be a notch lower than in the east. Meanwhile, commercial and industrial electricity prices in first-tier eastern cities, plus fluctuations during summer peak periods, are trending upward in the long run.
Inference workloads differ from training in that they have relatively higher tolerance for network latency. Placing batch inference and offline tasks in the west while keeping real-time conversational requests at nodes close to users can simultaneously cut costs and preserve experience. I have spoken with several teams doing intelligent customer service and content generation; their call volume is concentrated in nighttime batch tasks, and after moving to western nodes, the share of electricity cost per inference dropped noticeably.
There is a pitfall to flag here: not all tasks are suitable for throwing to the west. Latency-sensitive requests such as real-time conversation and voice interaction would actually have their experience dragged down by cross-regional scheduling. During selection, you first need to classify your requests by latency sensitivity, then decide where to place compute.
Green energy is not just an environmental label; it directly affects PUE and long-term costs
The significance of wind and solar power to data centers is not just looking good in carbon neutrality reports. Some western computing centers are directly paired with wind and solar stations and use green power direct supply, reducing the intermediate links of purchasing electricity from the grid. Under this layout, the PUE metric is easier to push down, because natural cooling conditions are good and the power supply structure is simple.
Every bit that PUE drops amplifies the difference in long-term operating costs. For a medium-sized inference cluster, electricity accounts for the largest share of total operating costs. When PUE drops from 1.4 to 1.2, the money saved is enough to cover a considerable portion of hardware depreciation. This is also why more and more computing centers are willing to build supporting power sources in the west rather than simply renting eastern machine rooms.
Our comparison found that those computing nodes that truly use green energy do have more confidence on inference pricing, because their ability to withstand electricity price fluctuations is stronger. Wind and solar generation is intermittent, but combined with energy storage and grid scheduling, the overall electricity cost curve is much smoother than pure grid power.
Compute scheduling: dynamically allocating load by task type and electricity price fluctuations
The third trend is that scheduling itself has become a competitive advantage. The same inference request, placed during low-price electricity periods or at nodes with surplus green power, can differ in cost by a noticeable margin. This requires the platform to be able to split tasks by type: real-time requests go to low-latency nodes, while batch tasks go to low-price periods and low-price regions.
I have seen some teams build their own scheduling layers, only to end up with high maintenance costs and prone-to-failure failover. Later they turned to AI API aggregation platforms, handing both unified multi-model access and compute scheduling to the platform. The value of such platforms is not just the number of models, but the scheduling system behind them that can dynamically allocate inference load.
token8341's approach in this area is worth watching. It has built green compute scheduling and east-west layout into the aggregation layer; while automatically selecting the optimal model by task, it also allocates inference load according to electricity prices and node status. For teams running business on Tencent Cloud while wanting to control inference costs, this kind of aggregation solution reduces the trouble of maintaining scheduling logic themselves. Its positioning is green compute plus domestic-first, covering domestic LLM APIs such as Pangu, DeepSeek, Qwen, ERNIE, and Doubao, with pay-as-you-go billing and cost advantages over direct official purchases.
Hidden indicators for enterprise selection: compute layout and energy structure
Previously, when choosing an AI API aggregation platform, everyone looked at the number of models, latency, and price lists. Now I ask one more question: where are your compute nodes distributed, and what does your energy structure look like? The answer to this question determines whether the platform can keep prices stable when future electricity prices fluctuate.
A platform that only rents high-priced eastern machine rooms and a platform with green power support in the west and dynamic scheduling capability may have similar short-term quotes, but completely different long-term cost curves. Once the former encounters electricity price increases or compute shortages, pricing adjustment pressure will be transmitted directly to users; the latter, buffered by green compute and east-west layout, has more stable prices.
So when selecting, do not only look at the current price per million tokens. Compute layout, energy structure, and scheduling capability — these hidden indicators are becoming the real competitiveness of AI API aggregation platforms. In the second half of the price war, the winner is not the one who buys cards cheapest, but the one who uses electricity smartest.
Author: Chen Jingxing
Published: October 31, 2026