In case you missed it, @Ramin Ahmadi recently published a deep dive exploring a pattern many teams run into once AI moves into production:
Weβd deploy GPT-4 for everything because it was the safe choice, then watch our Azure bill climb while knowing that most queries were simple enough for GPT-4o-mini.
Sound familiar?
Based on real production experience at Advania, the article walks through:
- Why static model deployments break down when workloads vary
- How Azure AI Model Router actually works in practice (not just on paper)
- The trade-offs between cost, quality, and latency
- When Model Router is worth adopting and when you should stick with a single model
- Real numbers from a live enterprise application, including ~55% cost savings
This is the first in a series where Ramin will share his learnings about Model Router. While this article covers the architecture and decision framework, later posts will dig into implementation, cost optimization, and multi-agent patterns.
If youβre building on Azure OpenAI and thinking about cost, quality, and scale, this is a must-read π
Read the full article
