Frugal AI: The Green Model Router

The Green Model Router

Frugal AI Router is an open-source proxy layer that sits between your application and AI providers. It analyzes incoming requests, benchmarks them against efficiency metrics, and routes them to the most resource-appropriate model. Think of it as a “green load balancer” for AI.

Core Components

• Request Analyzer: Classifies task complexity (simple Q&A vs. complex reasoning)
• Model Efficiency Index: Database of models ranked by performance-per-watt
• Smart Router: Directs requests to the most efficient capable model
• Dashboard: Visualizes energy savings, cost reduction, and carbon impact

Problem to Solve

1. Wasteful AI defaults: Developers use powerful models for simple tasks (summarization, classification, basic chat) that smaller models handle equally well
2. No visibility: There’s no easy way to measure or compare the environmental footprint of AI choices
3. Cost explosion: Overpowered model usage drives up API costs unnecessarily
4. Hosting blind spot: Web hosts offering AI features have no tools to optimize or report on sustainability

Target Audience

• Web hosting providers adding AI features to their platforms
• WordPress plugin developers integrating AI functionality
• Agencies and developers building AI-powered sites for clients
• Enterprise teams with sustainability reporting requirements
• Open-source projects wanting responsible AI integration
• …

Role	Contribution
Backend Developers	Build the routing logic, API proxy, model abstraction layer
Frontend/UI Developers	Create the dashboard and configuration interface
DevOps/Platform Engineers	Containerization, deployment, scaling considerations
Data/ML Enthusiasts	Help build the task classification and benchmarking system
Sustainability Advocates	Research carbon metrics, validate methodology
Technical Writers	Documentation, pitch materials, demo scripts

Hackathon Goals

1. Deliver an MVP routing proxy across multiple AI providers. Build a working proxy that can route requests between 2–3 model providers (e.g., OpenAI + Anthropic) and different models, with a stable API surface that teams can drop into existing apps.
2. Add an initial task complexity signal to drive routing. Implement a basic complexity classifier (rule-based or lightweight ML) that predicts “cheap vs. capable” needs and selects an appropriate model accordingly.
3. Make footprint and cost observable by default. Log every request/response with estimated cost and energy/CO₂ proxy metrics (even if coarse at first), ensuring audits and comparisons are possible.
4. Provide a minimal dashboard that proves value. Ship a simple dashboard that explains routing decisions and quantifies savings (cost, estimated energy/CO₂), so users can validate impact quickly.
5. Enable configurable routing strategies beyond the default. Support switchable policies (cost-first vs. green-first vs. performance-first) via configuration and rules, so teams can align routing with their priorities.
6. Lay the foundation for ecosystem adoption and benchmarking. Package integrations (e.g., a WordPress wrapper) and a model-efficiency benchmarking harness, with a path toward later stretch features such as a developer browser extension and a public “greenest implementation” leaderboard.