LLM Service Rate Limiting and Cost Estimation for Organizations
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Solution Overview
Problem
Existing cloud-based large language model (LLM) services face challenges in managing resource consumption and cost estimation, particularly when made available to all users, with no mechanisms for limiting requests or estimating costs effectively.
Innovation Solution
A system and method for providing LLM services at varying granularities (organization, application, and user levels) with rate limiting and cost estimation techniques, using a predictive model to suggest appropriate LLM models and enforce thresholds, supported by a graphical user interface for visibility and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If LLM services are made available to all users of an organization, then accessibility and usability are improved, but resource consumption and network bandwidth increase significantly
Solution Approach 1:
The patent segments LLM service access control into multiple hierarchical levels: organization-level policies, application-level policies, and user-level policies. This segmentation allows the system to provide broad accessibility while enabling fine-grained control over resource consumption at each level, resolving the contradiction between universal access and resource management.
Solution Approach 2:
The patent implements dynamic rate limiting mechanisms that adjust request thresholds based on organizational policies, application requirements, and user permissions. These dynamic thresholds allow the system to adapt resource allocation in real-time, providing high accessibility when resources are abundant while preventing overload when resources are constrained.
2Ease of operation
If no mechanisms are implemented for limiting requests, then ease of operation is maintained, but system reliability and resource management deteriorate
Solution Approach 1:
The patent implements self-service rate limiting where the system automatically enforces policies, monitors resource consumption, and manages quotas without requiring manual intervention. Administrators define high-level policies, and the system autonomously handles request validation, throttling, and enforcement, maintaining operational simplicity while ensuring reliable resource management.
Solution Approach 2:
The patent incorporates feedback mechanisms that continuously monitor LLM service usage patterns, resource consumption metrics, and policy compliance. This feedback enables the system to dynamically adjust rate limits, alert administrators to potential issues, and optimize resource allocation, thereby maintaining both simplicity and reliability.
3Use of energy by moving object
If rate limiting mechanisms are implemented, then resource consumption is controlled, but device complexity increases
Solution Approach 1:
The patent divides the rate limiting functionality into modular components distributed across different system levels: organization policy engines, application rate limiters, and user quota managers. This segmentation isolates complexity into manageable modules, each handling specific aspects of resource control, thereby reducing overall system complexity while maintaining effective resource management.
Solution Approach 2:
The patent introduces intermediary components such as policy evaluation services and rate limit enforcement proxies that mediate between LLM service requests and the underlying infrastructure. These intermediaries abstract the complexity of rate limiting logic from core system components, providing resource control without proportionally increasing overall system complexity.
4Measurement precision
If cost estimation techniques are provided, then decision-making accuracy is improved, but processing overhead increases
Solution Approach 1:
The patent performs cost estimation calculations in advance by analyzing historical LLM usage patterns, model pricing information, and organizational consumption rates. These preliminary cost estimates are cached and updated periodically, providing accurate cost projections without requiring real-time computation for each request, thus balancing accuracy with processing efficiency.
Data Source
AI summary
Apparatus and method for recommending and configuring LLM models for organizations. For example, LLM model usage requirements of one or more organizations are evaluated, including applications and users associated with each organization. A cost estimation is performed with respect to expected utilization of the plurality of LLM models and a subset of LLM models is recommended for each of the organizations, applications, and users, along with rate limits for each organization and corresponding applications based on a global threshold rate limit specified for the entity. Upon acceptance by an administrator, the global threshold rate limit is partitioned into a corresponding set of per-organization threshold rate limits; each organization threshold rate limit is allocated to a corresponding organization of the one or more organizations, and each respective threshold rate limit is subdivided into portions to be allocated to applications of the respective organization.