Thirty-two billion 令牌 per month is not a flex. At that scale, inference alone can run into hundreds of thousands of dollars, depending on the model, architecture, and call patterns. The real issue is usually not that AI is expensive. The issue is that large LLMs are being asked to do everything-including work that 确定性规则, database queries, scripts, or small local models could handle faster and cheaper.
I compare it to sending a heavy-duty lorry from 马六甲 to Kuala Lumpur just to deliver one small bag. The lorry can do it, but a Kancil will deliver the same item with far less fuel and cost. That is the idea behind 智能路由: assign the right engine to the right job.
At NeuralOps, the 独立系统 构建者 代理 uses local LLMs, Guard Rails, and 智能路由 to 设计 and assemble the 系统. The agent may burn through serious 令牌 during initial learning, development, testing, and validation-but that is temporary. It is the build phase, not the run phase.
Once the 独立系统 is complete, routine operations are pushed down to fixed rules, PHP services, automation scripts, databases, and validated 工作流. 从 then on, the 系统 does not need an LLM call for every transaction. Token are reserved for exceptions, 未知 cases, 系统 improvements, and the tasks that actually require AI.
The result: monthly usage can fall from about 32 billion 令牌 to roughly 1.5–2 billion 令牌. In many deployments, the core detached workflow itself can run for about USD20 per month, depending on infrastructure and workload.
Key outcomes we see in the field:
更低 token consumption at scale
更低 long-term operating costs
本地 LLM usage for tighter control
Guard Rails for predictable outputs
智能路由 that avoids oversized models
AI invoked only when it is genuinely required
分离式 工作流 that keep 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。 然后 the 系统 runs independently.
