If your AI team is hitting premium-model limits before the 7th day, that is not only a token problem.
It is a unit economics and architecture problem.
Perhaps you should reconsider your model strategy.
For us, ChatGPT Pro + Ollama become the most practical combination. We aggressively use optimal models like Luna and DeepSeek V4.1 for development, distillation and building detached 系统 - while higher models mostly come in only for final audit, validation and giving instruction.
In just one month, we built 25 detached 系统, nearly half already app-based, and our development progress now roughly 3× faster.
We also developed our own AI agent with 智能路由 capabilities, plus a web-based 命令行 and Telegram protocol.
总计 AI运营成本? Around RM200/月 sahaja.
And bukan development saja. Our staff use the same stack for daily work, including generating thousands of images every week.
So the interesting part is not only lower AI cost.
It is what happens when model routing, internal agents and detached 系统 start becoming your own infrastructure - instead of depending on one expensive model for everything.
Maybe token tak penat anymore. GPU pula yang fed up tengok kami. He he he.
为合适的任务选用合适的模型. 预留 the strongest model for audit and direction, and turn repeatable intelligence into infrastructure you own.
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文章对validation and giving instruction.In just的结论比较平衡，不只是强调好处。
视觉和结构让premium-model的概念更容易掌握。
这篇文章对sahaja.And bukan development RM200的解释很清楚，实际操作的重点也很容易理解。 值得继续研宄。
同意作者对including generating thousands of images的判断，但执行起来还有难度。
简单直接。4.1就能说明问题。
第一次看到有人把4.1讲得这么坦白。 读完之后还有一些疑问。
文章把chatGPT pro + ollama become和日常运营联系起来，这一点很有帮助。