@svpino: Production traffic is not uniform. You get a few requests that need your best model, but most are simple questions and …
Summary
The article discusses optimizing AI model usage in production by routing requests to appropriate models based on complexity, using TrueFoundry's Auto Routing to reduce costs by up to 80% while maintaining high quality.
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Cached at: 09/11/26, 10:41 PM
Production traffic is not uniform.
You get a few requests that need your best model, but most are simple questions and lookups you can solve with cheaper, faster models.
The most expensive mistake you can make today is sending every request to your strongest model.
You need routing. Period.
This is the simplest trick to improve the architecture of whatever you are building.
Please, don’t implement routing yourself. You don’t have to.
I’m currently working with TrueFoundry’s Auto Routing. It reads each request, classifies it as simple, medium, or complex, and sends it to the model assigned to that tier.
You have two choices:
-
Send every request to the free heuristic classifier to score signals such as technical vocabulary, code, prompt length, and multi-step reasoning.
-
Send the request to an LLM classifier when its difficulty requires a more nuanced judgment.
The beauty of using routing is that nothing changes in your code. You still call a single endpoint model, but routing works behind the scenes to pair every request with the best possible model.
TrueFoundry ran several experiments with two different setups:
- Send every request to Claude Opus
- Send every request to a router with Haiku, Sonnet, and Opus
The first experiment ran 550 deterministically graded academic prompts through every setup. Auto Routing was 69% cheaper while retaining 98% of the baseline quality.
The second experiment ran three production-shaped workloads through every setup, using user chats, developer chats, and long agent tasks. Auto Routing was 80% cheaper.
Thanks to the TrueFoundry team for partnering with me on this post.
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