"If you're building AI applications, you're probably juggling more than one LLM in your application: GPT4 for general tasks, Claude for coding, or maybe even the latest Nano Banana for image generation. During development, you might also want to experiment with open source models locally, if your hardware can handle it. But manually defining each model instance throughout your application can quickly make your code messy."
"And when it's time to deploy to production and use SOTA models in the cloud, the last thing you'd want is to rewrite half your codebase. In this article, we'll show a handy pattern for routing LLM calls in an "environment-aware" manner, using AI SDK's middleware. The idea is simple: the application environment (dev or prod) decides whether LLM queries are sent to a local model (like Llama 3.2) or a production model (GPT-5)."
"Instead of hardcoding model choices across our codebase, we'll centralize all models in a single file and then use AI SDK's middleware to route requests sent to the LLM. This way, the same code runs in any environment without changing imports or rewriting function calls. Flexibility to experiment There is a huge library of open source models you might want to try in your application."
Centralize model definitions and use AI SDK middleware to route LLM requests based on the runtime environment. Dev environments can target local or open-source models, while production environments can target cloud state-of-the-art models. The routing approach avoids hardcoding model instantiations across the codebase and eliminates the need to rewrite imports or function calls when switching environments. The pattern enables easier experimentation with different models, supports private and cost-effective local inference, and produces a single chatbot implementation that behaves appropriately in both development and production contexts.
Read at LogRocket Blog
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