# III. Many LLMs, One Intelligence Due to the first principle of modularity of the Seven-Factor App, an enterprise generative AI application should not rely on one single provider, model or version of a Large Language Model (LLM). There are several reasons for this but the three most common ones include 1. The evolution for fast LLM model iterations. 2. Pricing. 3. Requirements of certain agents and information to reside locally in a VPC or in a geo. 4. The requirement to fine-tune smaller models for highly specific tasks. The ensemble of LLMs can be spread across multiple agents or similar to the information layer be discoverable, fine tuned and then deployed using technologies similar to AWS's Bedrock, Azure AI Studio, Nvidia's Inference Microservices (NIMs) platform or Google's Vertex AI. A Seven-Factor App may include either a specialized agent, graph or an LLM router to dynamically route queries and requests to other agents or LLMs. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.sevenfactor.app/iii.-many-llms-one-intelligence.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.