Behind EcoPrompt: How I modeled the hidden physical cost of AI prompts (Zero Telemetry)

GenAI web interfaces (ChatGPT, Gemini, Claude) are pristine, minimal, and fast. But behind every single prompt lies a massive array of GPU clusters consuming real electricity, requiring evaporative cooling water, and generating carbon emissions.

I created EcoPrompt, an open-source Chrome Extension, to make this invisible footprint transparent to users in real time.

Before diving into the methodology, here is a quick 30-second promo teaser giving an overview of the concept:

(Watch on YouTube: EcoPrompt Concept Teaser)

❓ Why I Built This

Most environmental discussions around AI fall into two extremes: complete ignorance of resource consumption or overwhelming guilt trips about using modern tools.

I wanted a middle ground: unobtrusive, guilt-free awareness.

1. Physical Transparency: When you send a prompt, you should know if that specific request cost a teaspoon of water or a full glass.
2. Behavioral Nudging: Seeing real-time metrics encourages better prompting habits — batching queries, choosing lightweight models (Flash/Haiku) for simple tasks, and saving heavy reasoning models (o1/Opus) or image generation for when they are truly needed.
3. Financial Alignment: Bridging the gap between free web interfaces and underlying API token costs.

🔬 What Is EcoPrompt Based On?

The metrics aren't arbitrary guesses. They rely on peer-reviewed research and industry sustainability reports:

• Water Consumption (Scope 1 & 2): Based on research from UC Riverside ("Making AI Less Thirsty", Li et al.), combining direct evaporative cooling at the datacenter with indirect water used for electricity generation.
• Energy & Carbon Footprint: Calibrated using research from Hugging Face ("Power Hungry Processing", Luccioni et al.) alongside regional grid carbon intensity averages and provider datacenter efficiency metrics (PUE).
• Tiered Model Granularity: Different model architectures are categorized into distinct compute tiers:
  • Lightweight (GPT-4o mini, Gemini Flash, Claude Haiku): ~3–5 mL water / ~0.0005 kWh
  • Standard (GPT-4o, Gemini Pro, Claude Sonnet): ~20–30 mL water / ~0.003 kWh
  • Reasoning / Extended Thinking (o1/o3-series, Claude Opus/Thinking): ~120–250 mL water / ~0.02 kWh
  • Image Generation (DALL-E 3, Imagen 3): ~250–400 mL water / ~0.035 kWh

🛡️ Privacy & Architecture Choice

Building an extension that interacts with pages like chatgpt.com or claude.ai carries a heavy privacy responsibility.

To ensure 100% user privacy:

• Zero Telemetry: No tracking, no external analytics server, no remote APIs.
• Client-Side Only: Character length and model types are parsed in temporary browser memory.
• Local Persistence: All history and trends stay in your browser's chrome.storage.local.
• Manifest V3 & Vanilla JS: Lightweight footprint with no external npm dependencies.

🔗 Try It & Explore

EcoPrompt is completely free and open-source under CC BY-NC-SA 4.0.

• 📦 Install from Chrome Web Store: EcoPrompt on Chrome Web Store
• 💻 Source Code & Documentation: GitHub Repository
• ☕ Support Development: Ko-fi Page

How do you approach tracking or optimizing your daily AI usage? Let's discuss in the comments!