Use the right model for the right job: a unit economics approach to AI✎ Edit

If your team is exhausting premium-model quotas before the seventh day, the token count is not the real problem.

It is a unit economics and architecture problem.

What needs to change is how you allocate spend across your models.

For us at AINNA, ChatGPT Pro + Ollama has proven to be the most practical combination. We route the bulk of the workload - development, distillation and building detached systems - to efficient models like Luna and DeepSeek V4.1. The premium models come in only for final audit, validation and direction, which is precisely where they earn their cost back.

In a single month, we delivered 25 detached systems, nearly half of them already app-based, and development throughput is now roughly 3× faster - a multiplier that shows up directly in our cost per output.

We also built our own AI agent with smart routing capabilities, alongside a web-based CLI and a Telegram protocol. In accounting terms, these are internal assets we own, not recurring fees paid out to someone else.

Total AI operating cost? Around RM200/month sahaja.

And this is not limited to development. Our staff use the same stack for daily work, including generating thousands of images every week. Because the cost is shared across teams, the per-unit economics only improve as usage grows.

So the interesting part is not only the lower AI cost.

It is what happens when model routing, internal agents and detached systems start becoming your own infrastructure - instead of one expensive model carrying every function for everything.

Maybe the tokens no longer get tired. These days it is the GPU that is fed up watching us. 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.