Why I built EcoPrompt: A zero-telemetry Chrome extension to measure LLM compute costs

GenAI interfaces like ChatGPT, Google Gemini, and Claude are clean and instant. But behind every prompt lies power-dense GPU clusters drawing electricity and evaporative cooling water.

To bring physical transparency to AI interactions without tracking users or slowing down the browser, I built EcoPrompt — an open-source, 100% client-side Chrome extension (Manifest V3).

🌿 What EcoPrompt Tracks

When you hit Enter on ChatGPT, Gemini, or Claude, EcoPrompt displays a non-intrusive in-page toast estimating:

🍃 Carbon Footprint (g CO₂e): Real-time emissions based on datacenter PUE and energy grid averages.
💧 Water Consumption (mL): Direct evaporative cooling plus indirect electricity water draw.
⚡ Active Energy (Wh): Compute energy required for prompt prefill and decode cycles.
💶 API Cost Equivalence: Baseline token computation rates across model tiers.

🛠️ Technical Constraints & Architecture Choices

Building a extension for developers required strict design decisions:

1. Zero Telemetry & 100% Client-Side Privacy

No prompt logging: Prompt text is never stored, read, or transmitted.
In-memory evaluation: Character counts are held temporarily in memory to apply KV-cache weightings, then immediately purged.
Local persistence: All analytics stay local via chrome.storage.local.

2. Pure Vanilla Architecture (< 150 KB)

Built with zero external dependencies (no npm bloat).
Dynamic timelines (Day, Week, Month, Year) run on a custom-built, lightweight native SVG charting engine taking under 10 KB.