AI Agent Energy & Carbon Cost Calculator
Estimate electricity cost and carbon footprint for local and cloud AI agent inference. Compare cloud API energy per token against local GPU TDP, PUE, grid carbon intensity, and projection months.
Scenario preset
📊 Workload
Energy / month
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Electricity cost / month
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Carbon / month
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Cost per 1k requests
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Context
- Equivalent US homes / month—
- Equivalent car miles / month—
- Total tokens / month—
- Energy per 1k output tokens—
Verdict
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How to use these numbers
- Use cloud mode to estimate the hidden energy and carbon cost behind API calls, including datacenter overhead.
- Use local mode to size PSU, cooling, and monthly electricity budget for a self-hosted inference rig.
- Compare different regions or renewable offsets before committing to a hosting location or colo.
- Track cost per 1k requests and kg CO₂ per 1k requests as KPIs alongside latency and accuracy.
Last updated: 2026-07-20. See notes.
Frequently asked questions
How accurate is the energy estimate?▼
These are order-of-magnitude estimates. Cloud values use per-token energy factors derived from public MLCommons-style datacenter inference benchmarks and include a PUE multiplier. Local values use hardware TDP and an assumed active utilization percentage; real draw depends on workload, batching, quantization, and cooling.
Does cloud or local have a lower carbon footprint?▼
It depends on scale, hardware efficiency, and your local grid. A small-volume agent on an efficient cloud model in a low-carbon region is usually cleaner than a high-TDP local GPU running 24/7 on coal-heavy power. For high volume, optimized local inference on efficient hardware in a clean grid can win.
What is PUE?▼
Power Usage Effectiveness (PUE) is total datacenter energy divided by IT equipment energy. A PUE of 1.2 means cooling, networking, and power delivery add 20% on top of the compute itself. Cloud providers typically report 1.1–1.3; home setups are closer to 1.0–1.15 for the machine itself.
How can I reduce AI agent energy cost?▼
Use smaller or quantized models, cache prompts and embeddings, batch requests, run on efficient hardware, choose a low-carbon grid or renewable energy, and compress context to reduce tokens per request.
Why include carbon intensity?▼
The same kilowatt-hour creates very different emissions depending on whether it comes from hydro, nuclear, gas, or coal. This calculator lets you model your actual grid or a target green region.
Energy and carbon estimates are rough orders of magnitude. Cloud figures use provider-reported or MLCommons-style inference energy per 1k tokens and include PUE. Local figures use GPU/CPU TDP and assumed utilization. Real-world values vary with model size, quantization, batching, cooling, and hardware generation.