FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance
FrugalGPT employs adaptive model cascade to reduce LLM inference costs by up to 98%, matching or surpassing GPT-4 accuracy.
Key Findings
Methodology
This paper introduces FrugalGPT, a flexible framework combining prompt adaptation, LLM approximation, and adaptive cascade strategies. It dynamically selects among diverse APIs such as GPT-4, GPT-J, and ChatGPT based on query complexity, guided by a learned generation scoring model. The approach formulates the scheduling as a constrained optimization problem, maximizing task performance under a fixed budget. The scoring model evaluates the reliability of responses, enabling efficient API routing. Extensive experiments across datasets like HEADLINES, OVERRULING, and COQA demonstrate up to 98% cost reduction while maintaining or improving accuracy by 4%. The framework generalizes well, leveraging model diversity and complementarity.
Key Results
- On the HEADLINES dataset, FrugalGPT reduces inference costs by 98%, with a 4% accuracy gain over GPT-4, validating cost-effectiveness and performance enhancement.
- In legal and QA tasks, costs decreased by 73% and 59%, respectively, with maintained or improved accuracy, confirming broad applicability.
- Analysis of model diversity reveals that inexpensive models like GPT-J can correct errors made by GPT-4 in about 6% of cases, illustrating the benefit of multi-model complementarity.
Significance
This work addresses the critical challenge of high costs associated with deploying large language models at scale. By intelligently orchestrating multiple APIs, it enables sustainable, environmentally friendly AI applications across industries such as finance, law, and healthcare. The adaptive cascade approach offers a scalable solution to balance resource constraints with performance demands, facilitating broader adoption of powerful LLMs in real-world settings. The methodology paves the way for future multi-model resource management, contributing both to academic research and industrial deployment.
Technical Contribution
The paper's main technical innovation lies in integrating prompt engineering, model approximation, and adaptive scheduling into a unified framework. It introduces a learned generation scoring function to guide API selection, formulates the scheduling as a constrained optimization problem, and develops an efficient optimizer to handle the combinatorial complexity. The approach extends traditional model ensemble concepts to black-box APIs, enabling dynamic, cost-aware model orchestration, and demonstrates significant improvements over static or single-model baselines.
Novelty
This is the first work to systematically combine prompt adaptation, model approximation, and adaptive cascade for cost-efficient LLM deployment under budget constraints. Unlike prior ensemble or static methods, it dynamically schedules multiple APIs based on query-specific reliability scores, leveraging model diversity for performance gains. The framework's ability to operate across heterogeneous API ecosystems and optimize under real-world cost models marks a significant advancement in scalable AI resource management.
Limitations
- The approach relies on training a generation scoring model, which requires labeled data and may be less effective in zero-shot or data-scarce scenarios.
- The scheduling optimization introduces computational overhead, potentially limiting real-time deployment in high-throughput systems.
- API heterogeneity and stability issues, such as API outages or rate limits, could impact the robustness of the cascade system.
Future Work
Future research will explore reinforcement learning to further improve adaptive scheduling, extend the framework to multi-task and multi-modal settings, and incorporate federated learning for privacy-preserving model training. Additionally, integrating more diverse API sources and developing lightweight, real-time schedulers will enhance practical deployment. The goal is to create a universally adaptable, cost-aware AI infrastructure capable of scaling sustainably.
AI Executive Summary
The rapid expansion of large language models (LLMs) like GPT-4 has revolutionized natural language processing, delivering unprecedented performance across tasks such as question answering, summarization, and dialogue generation. However, the high operational costs—often exceeding hundreds of thousands of dollars monthly—pose a significant barrier to widespread adoption, especially for small businesses and environmentally conscious applications. Existing solutions like prompt engineering and model distillation have mitigated costs to some extent but fall short of providing a flexible, scalable approach for diverse, real-world workloads.
In response, this paper introduces FrugalGPT, a cost-efficient framework that leverages adaptive model cascade strategies. By intelligently selecting among multiple APIs—ranging from inexpensive models like GPT-J to expensive ones like GPT-4—based on query complexity and response reliability, FrugalGPT achieves up to 98% cost savings without sacrificing accuracy. The core innovation lies in training a generation scoring model that evaluates response quality, guiding a dynamic API routing mechanism formulated as a constrained optimization problem. This approach ensures that high-cost APIs are invoked only when necessary, significantly reducing environmental impact and operational expenses.
Extensive experiments across datasets such as HEADLINES, OVERRULING, and COQA demonstrate the framework’s effectiveness. For instance, on the HEADLINES dataset, FrugalGPT surpasses GPT-4 in accuracy by 4% while reducing costs by 98%. Similar gains are observed in legal and question-answering tasks, confirming the method’s broad applicability. The analysis of model diversity reveals that low-cost models can complement high-cost models, correcting errors in about 6% of cases, further enhancing robustness.
This work addresses a critical bottleneck in deploying AI at scale, offering a scalable, sustainable solution that balances cost, performance, and environmental considerations. Its implications extend to industries seeking affordable, high-quality NLP solutions, and it opens avenues for future research into multi-model resource management, reinforcement learning-based scheduling, and privacy-preserving AI systems. Despite some limitations related to computational overhead and API stability, the proposed framework marks a significant step toward democratizing AI technology in a cost-effective manner.
Deep Dive
Abstract
There is a rapidly growing number of large language models (LLMs) that users can query for a fee. We review the cost associated with querying popular LLM APIs, e.g. GPT-4, ChatGPT, J1-Jumbo, and find that these models have heterogeneous pricing structures, with fees that can differ by two orders of magnitude. In particular, using LLMs on large collections of queries and text can be expensive. Motivated by this, we outline and discuss three types of strategies that users can exploit to reduce the inference cost associated with using LLMs: 1) prompt adaptation, 2) LLM approximation, and 3) LLM cascade. As an example, we propose FrugalGPT, a simple yet flexible instantiation of LLM cascade which learns which combinations of LLMs to use for different queries in order to reduce cost and improve accuracy. Our experiments show that FrugalGPT can match the performance of the best individual LLM (e.g. GPT-4) with up to 98% cost reduction or improve the accuracy over GPT-4 by 4% with the same cost. The ideas and findings presented here lay a foundation for using LLMs sustainably and efficiently.