Contextual Pricing Model for Generative AI Credit Metering
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Solution Overview
Problem
Integrating generative artificial intelligence (AI) solutions into cloud computing systems is challenging due to differences in usage models compared to traditional software applications, making it difficult to implement a unified charging model.
Innovation Solution
A system that integrates generative AI solutions into cloud computing by using a contextual pricing model to track usage and convert it into a unified credit-based model, allowing for different charging methodologies to be unified into a single billing structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional per-user licensing is used for cloud software applications, then billing is simple and straightforward, but it cannot accommodate the usage-based charging model of generative AI solutions
Solution Approach 1:
The patent introduces a contextual pricing model as an intermediary layer between diverse generative AI charging methodologies and the unified cloud billing system. This model translates various usage metrics (tokens, requests, time) into a standardized credit system, enabling adaptability without directly complicating the core billing infrastructure
Solution Approach 2:
The system dynamically changes pricing parameters based on contextual factors such as usage type, time of day, and resource consumption. By adjusting these parameters within the contextual pricing model, the system adapts to different generative AI charging requirements while maintaining a consistent billing interface
2Adaptability or versatility
If multiple different charging methodologies are supported for various generative AI solutions, then versatility is improved, but the system complexity increases
Solution Approach 1:
The patent segments the billing system into distinct functional layers: the contextual pricing model handles diverse charging methodologies, while the underlying credit-based billing system maintains simplicity. This segmentation allows each layer to specialize without propagating complexity throughout the entire system
Solution Approach 2:
The credit-based billing system serves as a universal intermediary that can accommodate multiple charging methodologies. By converting all generative AI usage types into credits, the system achieves multi-functionality without requiring separate billing mechanisms for each usage pattern
3Measurement precision
If usage tracking is implemented for different generative AI solutions with different charging models, then accurate billing is achieved, but tracking complexity increases
Solution Approach 1:
The contextual pricing model acts as a tracking intermediary that standardizes diverse usage metrics into uniform credit measurements. This intermediary layer ensures accurate tracking of different usage types (tokens, requests, time) without requiring the core billing system to handle each metric type separately
4Ease of operation
If a unified billing structure is implemented for generative AI, then integration into cloud computing is simplified, but flexibility in supporting different charging methodologies may be reduced
Solution Approach 1:
The contextual pricing model enables parameter changes within the unified billing structure, allowing different charging methodologies to be represented as varying parameters (token counts, request frequencies, time durations) that all convert to the same credit unit. This maintains integration simplicity while preserving charging flexibility
Data Source
AI summary
In some embodiments, a method stores a total number of generative credits for a generative artificial intelligence (AI) solution that is integrated with a software application in a database system. Usage data is tracked for a request to the generative artificial intelligence (AI) solution in the database system. The method determines a context from the usage data and retrieves a contextual pricing model for the generative AI solution using the context. The contextual pricing model translates a model specific charging policy to generative credits. The method applies the usage data to the contextual pricing model to translate the usage data to a number of generative credits. The number of generative credits for the generative AI solution is applied to an available number of generative credits of the total number of generative credits to generate a new available number of generative credits.