Footprint AI

Every AI query uses real energy, real water, and produces real CO₂. Footprint AI makes that invisible cost visible. Footprint AI estimates the energy consumed by your AI usage across platforms including Claude and ChatGPT. Power consumption, GPU compute, datacenter water consumption for cooling, and carbon dioxide equivalent emissions, translated into real-world comparisons you can feel: glasses of water, phone charges, and driving distance. How it works Footprint AI detects queries across 30+ AI platforms and estimates their energy, water, and CO₂ cost using published research from Epoch AI, the IEA, and peer-reviewed studies on AI inference. Estimates account for GPU compute, datacenter cooling, and upstream electricity generation. All processing runs locally in your browser. What it estimates • Energy consumption (Wh / kWh) • Datacenter water consumption (mL / L / gal) • CO₂ equivalent emissions (mg / g / kg) • Real-world comparisons: shower time, cell phone charges, driving distance • GPU compute time (GPU-ms / sec / min / hr) Key features • Session + lifetime tracking: session resets like a trip odometer, lifetime accumulates forever • Duration-scaled estimates: longer AI responses register higher energy use • Dark mode: toggle between light and dark themes • Units toggle (metric / imperial) • 30+ platforms supported: text, image, video, and audio AI tools • 100% local: no accounts, no servers, no data leaves your device • 8 languages (English, Japanese, Spanish, Chinese, French, German, Portuguese, Korean) Privacy Footprint AI collects zero data. Everything is processed and stored locally on your device using the browser's built-in storage. No account required. No network requests. Uninstalling removes everything. Your AI usage is your business — we just help you see its footprint. Methodology Estimates are based on published research: Epoch AI (2025) for inference energy, Luccioni et al. (2024) for model-specific benchmarks, IEA (2024) for datacenter energy overhead, and EPA eGRID (2023) for grid emissions factors. These are order-of-magnitude estimates. Actual usage varies by model, prompt length, and datacenter efficiency.