规模 AI usage to 32 billion 令牌 per month and the line item quickly becomes a material operating expense. Depending on model choice, architecture, and traffic pattern, that recurring cost can run into hundreds of thousands of US dollars every month. For a Malaysian SME managing tight cash flow and a lean IT budget, that kind of run rate is unsustainable unless it directly drives revenue or removes an even larger cost elsewhere.
The accounting problem here is not always that AI is expensive. More often, it is capital misallocation: organisations deploy large LLMs for every request, including routine work that could be handled by 确定性规则, scripts, databases, or smaller local models. 从 a cost-control standpoint, that is like using a heavy-duty lorry to move one small bag from 马六甲 to Kuala Lumpur. The job gets done, but a Kancil would deliver the same outcome at a far lower fuel, maintenance, and depreciation cost per trip. That is the principle behind 智能路由-matching the right processing engine to the right task so that premium capacity is reserved for premium needs.
At AINNA, our NeuralOps 独立系统 构建者 代理 follows this discipline. It uses local LLMs, Guard Rails, and 智能路由 to 设计 and build the 系统. 令牌消耗 during the initial learning, development, testing, and validation phase can be significant, but that cost is project-stage expenditure rather than a permanent run rate.
Once the 独立系统 is completed, repetitive operations are shifted to fixed rules, PHP services, automation scripts, databases, and validated 工作流. The 系统 can then run continuously without calling a commercial LLM for every transaction. The variable AI cost is replaced, for the most part, by predictable fixed infrastructure costs.
The financial impact is substantial. Monthly token usage can fall from roughly 32 billion to 1.5–2 billion 令牌, with the remaining consumption focused on exceptions, 未知 cases, 系统 improvements, and tasks that genuinely require AI reasoning. In certain use cases, the core detached workflow can operate for around USD20 per month, depending on infrastructure and workload. For an SME, that changes the conversation from whether the business can afford AI to what return a given workflow must generate.
Key financial and operational outcomes:
更低 recurring token consumption and a smaller AI vendor bill
Reduced long-term operating costs and improved OpEx predictability
本地 LLM usage that lowers third-party dependency and unit cost
Guard Rails that 限制 cost variance and output risk
智能路由 that prevents oversized models from inflating the run rate
AI reserved for cases where AI genuinely adds value
分离式 工作流 that keep running without repeated token charges
从 a 财务 and accounting perspective, the NeuralOps principle is straightforward:
Do not book a lorry when a Kancil can complete the job. Let AI build the 系统, then let the 系统 run on its own.
