Idea
A platform that routes AI queries to cost-effective language models with user-controlled quality-cost balance for enterprises and developers
Research Paper
Core Innovation
This paper introduces IPR, a modular framework that intelligently routes prompts to the most cost-effective large language model while maintaining user-specified quality levels. It uniquely combines quality estimators trained on a large prompt dataset with a user-controlled routing mechanism, enabling dynamic quality-cost trade-offs. The design also supports rapid integration of new models, improving adaptability over prior static or single-model approaches.
Market Size (TAM)
$2–10B TAM, $1–2B SAM; assumption: growing adoption of LLMs in enterprises and AI services requiring cost-quality optimization.
Potential Customers & Pain Points
- Enterprises managing multiple LLMs seeking cost efficiency
- AI developers needing quality-cost trade-off control
- SaaS providers optimizing AI query costs
Business Model
Subscription-based API access with tiered pricing based on query volume and quality-cost customization features
Competitive Landscape
- OpenAI API
- Cohere
- Anthropic
Implementation Challenges
- Accurate real-time quality estimation
- Integration complexity with diverse LLMs
- User adoption of quality-cost trade-off controls
Validation Strategy
- Pilot with enterprise AI teams to measure cost savings and quality retention
- Benchmark against existing LLM routing or selection methods
- Collect user feedback on quality-cost trade-off usability
Research Paper Overview
IPR: Intelligent Prompt Routing with User-Controlled Quality-Cost Trade-offs
Summary
IPR is a framework that routes queries to the most cost-effective large language model while maintaining response quality based on user-specified tolerance. It features modular quality estimators trained on 1.5M prompts, a user-controlled routing mechanism with a quality-cost trade-off parameter, and an extensible design that accelerates new model integration. Evaluated on a large benchmark with 11 LLMs, IPR reduces costs by 43.9% with sub-150ms latency and quality parity to top models.