Configuration-Based LLM Routing Across Hybrid and Multi-Cloud
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Solution Overview
Problem
Developers face challenges in creating applications that seamlessly operate across different cloud architectures, including single cloud, hybrid cloud, and multi-cloud setups, due to the complexity of establishing a resilient infrastructure for generative artificial intelligence (GenAI).
Innovation Solution
A configuration-based endpoint determination system is implemented, utilizing a standard input contract and a unified payload structure to select and authenticate large language models (LLMs) based on requester identity, task type, and payload, enabling seamless switching between on-premises and cloud platforms while ensuring access control, governance, ethics, and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If developers create applications that harness GenAI across different cloud architectures, then the applicability and user-friendly nature of GenAI is improved, but the infrastructure complexity and development challenges increase significantly
Solution Approach 1:
The patent introduces an intermediary layer (the system with processors and storage media) that sits between the requester and multiple LLM providers. This intermediary handles authentication, authorization, model selection, and request transformation, shielding developers from the complexity of directly managing multiple cloud architectures while enabling seamless access to GenAI capabilities across single-cloud, hybrid-cloud, and multi-cloud setups
2Adaptability or versatility
If a system supports multiple large language models across hybrid and multi-cloud setups, then the versatility and interoperability are improved, but the system complexity and configuration difficulty increase
Solution Approach 1:
The patent creates a universal system that can work with multiple LLM providers and support various cloud architectures through a single unified interface. The system performs multiple functions including authentication, authorization, model selection based on task requirements, and request transformation, all through one configuration-based framework that eliminates the need for separate configurations for each cloud provider or model
Solution Approach 2:
The system uses configuration-based parameters to dynamically select and transform requests for different LLMs. By changing parameters such as model identification, authentication credentials, and request format based on the selected model and task type, the system adapts to different cloud architectures without requiring structural changes to the core system
Data Source
AI summary
An example computer system for selecting artificial intelligence can include: one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to: receive a request from a requester for a task to be performed by a plurality of large language models; authenticate the requester; authorize the task based upon a type of the task and access for the requester to the plurality of large language models; select one of the plurality of large language models based upon the requester, the task, the type of task, and a payload associated with the task; transform the request to a transformed request based upon the one of the plurality of large language models; and forward the transformed request to the one of the plurality of large language models to perform the task.