How much IT cost is required at least to build an immediately commercializable AI application overseas?

When global AI application developers set their sights on overseas markets, "high commercialization costs" and "long payback periods" have become the core challenges standing in the way of large-scale implementation. During WAIC 2025, GMI Cloud officially launched its self-developed "AI Application Construction Cost Calculator", which provides cost planning support for developers by quantifying in real-time the computing power costs, time losses, and cost-effectiveness of suppliers in different scenarios.

According to data from artificialanalysis.ai and GMI Cloud's assessment of typical scenarios (such as code-building), using the GMI Cloud solution can reduce overseas IT costs by more than 40% and shorten the payback period to one-third of the industry average.

I. Economic and Time Costs of Overseas AI Application Commercialization: Token Consumption is the Deep End, and There is a Long Journey from Technical R & D to Market Validation

As the basic unit of AI text processing, the consumption cost of Tokens directly determines the commercial feasibility. In the wave of global AI applications going overseas, the black hole of dynamic Token consumption costs and the time losses of starting from scratch in R & D are becoming the core pain points for enterprises. According to industry data, GPT - 4 Turbo can consume up to 2 million Tokens (costing about $2) when processing a single multi-step Agent task, and the engineering deployment cycle is generally underestimated by 60%.

In the traditional model, the Token cost is like a bottomless pit. For example, to generate a 1,000 - word copy, GPT - 4 Turbo consumes $0.12. Other languages may consume 20% - 50% more Tokens than English for the same text due to the complexity of word segmentation. With mechanisms like the sliding window, the actual consumption surges by 40% when processing a 10K Token document, which is almost impossible to capture through manual calculation.

Meanwhile, the Token throughput speed has become an "invisible timer" for determining the construction of AI applications and AI Agents. Builders generally underestimate the impact of Token processing efficiency on the R & D cycle, causing many AI applications to miss the best market window period. When a leading e - commerce enterprise was developing an intelligent customer service AI, it originally planned to base it on an open - source model and launch the application within 6 months. However, in actual R & D, due to the large volume of dialogue data, the number of Tokens processed per second by the model was far lower than expected. It took several weeks to train a single optimized version. Especially in multiple rounds of iteration, due to insufficient Token processing efficiency, delays frequently occurred in links such as data cleaning, model fine - tuning, and deployment. Eventually, the project took 18 months to deliver, three times longer than the original plan, and missed many market commercialization opportunities.

The innovation of GMI Cloud's "AI Application Construction Cost Calculator" lies in its dual - track accounting mechanism. Based on the number and unit price of Tokens (distinguishing between input/output), it calculates the total cost of constructing AI applications/AI Agents; combined with the Token throughput (input/output speed), it calculates the time required to process all requests. At the same time, this tool can also compare in real - time the Token unit prices of 15 suppliers such as OpenAI and Anthropic and automatically mark low - cost alternative solutions such as Inference Engine.

"We found that although the unit price of some large - model inference API services is low, the insufficient throughput leads to a sharp increase in service duration, which actually drives up the hidden costs of AI application construction," said Yujing Qian, the technical VP of GMI Cloud. "The calculator helps customers see through the 'low - price trap' and truly optimize the TCO (Total Cost of Ownership)."

II. From Cost Calculator to Commercialization Accelerator: GMI Cloud Inference Engine

Many people think that cheap means slow, but that's not the case. According to practical data, the GMI Cloud Inference Engine can process data at a throughput of 161 tps per second, and it only takes a little over 15 hours to complete an output task of 9 million words. Some service providers, although offering low prices, can only process 30 words per second. The same task would take 83 hours (equivalent to 3 and a half days) to complete, seriously affecting business efficiency. For example, suppose you want to develop a code - assisted development tool that processes 10,000 requests per month, with each request having an input of 3,000 words and an output of 900 words. Using GMI Cloud would cost a total of $30.3 and the task can be completed in 15 and a half hours; while using a well - known cloud service would cost $75 (about 520 yuan) and take more than 40 hours to complete.

All of this is thanks to the GMI Cloud Inference Engine's underlying call of GMI Cloud's full - stack capabilities. It calls NVIDIA H200 and B200 chips at the bottom layer and has been optimized end - to - end from hardware to software, extremely optimizing the Token throughput per unit time to ensure the best inference performance and the lowest cost, and maximizing the help for customers to improve the load speed and bandwidth during large - scale work. At the same time, it allows enterprises and users to deploy quickly. After selecting a model, they can expand immediately, start the model within a few minutes, and directly use this model for Serving.

III. Quickly Start to Experience GMI Cloud's "AI Application Construction Cost Calculator"

The GMI Cloud "AI Application Construction Cost Calculator" tool is extremely easy to use. Users only need to simply select the "Agent scenario" and the "estimated total number of requests" to quickly obtain the "time required" and "cost" for AI application construction. In addition, they can freely set various parameters such as average input and output, which is both simple to use and flexible and accurate.