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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 systems — while higher models mostly come in only for final audit, validation and giving instruction.

In just one month, we built 25 detached systems, nearly half already app-based, and our development progress now roughly 3× faster.

We also developed our own AI agent with smart routing capabilities, plus a web-based CLI and Telegram protocol.

Total AI operating cost? Around RM200/month 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 systems 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.

Use the right model for the right job. Reserve the strongest model for audit and direction, and turn repeatable intelligence into infrastructure you own.