Depending on the model, architecture, and usage pattern, the operating cost can easily reach hundreds of thousands of dollars. 问题 is not always that AI is expensive. Often, the real problem is that companies use large LLMs for every task, including work that could be handled by simple rules, scripts, databases, or smaller local models.
It is like using a heavy-duty lorry to deliver one small bag from 马六甲 to Kuala Lumpur. The lorry can complete the job, but a Kancil could deliver the same item with far lower 燃料消耗 and operating cost. 这就是 principle behind 智能路由: use the right processing engine for the right task.
At NeuralOps, our 独立系统 构建者 代理 uses local LLMs, Guard Rails, and 智能路由 to 设计 and build the 系统. The agent may consume significant 令牌 during the initial learning, development, testing, and validation stage—but this usage is temporary.
Once the 独立系统 is completed, repetitive operations are transferred to fixed rules, PHP services, automation scripts, databases, and validated 工作流. The 系统 can then run continuously without requiring an LLM to process every transaction.
As a result, monthly usage could drop from approximately 32 billion 令牌 to only 1.5–2 billion 令牌, mainly for exceptions, 未知 cases, 系统 improvements, and tasks that genuinely require AI. In some use cases, the core detached workflow itself may operate for around USD20 per month, depending on infrastructure and workload.
Key outcomes:
更低 token consumption
更低 long-term operating costs
本地 LLM usage for better control
Guard Rails for predictable outputs
智能路由 to avoid oversized models
AI used only when AI is genuinely required
分离式 工作流 that continue running without repeated token charges
The NeuralOps principle is simple:
Do not use a lorry when a Kancil can complete the job. AI 构建系统。 The 系统 then runs independently.
