Why I started experimenting with cost-aware model routing

When an AI system reaches production, model selection stops being just a technical choice. It becomes a cost decision.

That’s why I started experimenting with Model Routing.

Why should every request go to the strongest model?

Some requests actually need strong reasoning.

Others are simple enough that a smaller model can handle them perfectly well.

So I started adding cost-aware model routing.

Instead of sending every request to the strongest model, the router decides between:

The idea is simple:

Use the cheapest model that can still meet the required quality.

A hypothetical example

Smaller model → $0.002/request
Stronger model → $0.02/request

Now imagine 10,000 requests/day.

If every request goes to the stronger model:

10,000 × $0.02 = $200/day

But if 80% of the requests can be handled by the smaller model:

8,000 × $0.002 = $16
2,000 × $0.02 = $40
Total = $56/day

That means:

That’s approximately:

• Daily savings → 72%
• Monthly savings → ~$4,320
• Yearly savings → ~$52,560

Illustrative model-call costs, not measured production results. Monthly savings assume 30 days; yearly savings assume 365 days. Routing and retry costs are excluded.

Same 10,000 requests.

Very different infrastructure cost.

But the interesting part isn’t writing the routing logic.

It’s deciding:

What actually makes a request “simple”?

• Task type?
• Reasoning complexity?
• Context size?
• Latency requirements?
• Confidence?

That’s where model routing becomes more than an if statement.

The routing logic is easy.
The decision boundary is the real engineering challenge.

#AgenticAI #AIEngineering #LLM #ModelRouting #SoftwareArchitecture