The Energy Behind Your AI Assistant
The Energy Behind Your AI Assistant
You asked, or we think you might. So here’s what running Aventide actually costs, in watts and water, not just dollars.
Short version: even if you used your entire monthly plan, it adds up to less energy than running a laptop for a few hours to about a day. Here’s the math, shown honestly, including the parts we can’t fully verify.
Starter · $39/mo
40,000 credits · ~0.5–1.0 kWh · ~0.3–4 L
A laptop for a few hours to half a day.
Pro · $99/mo
120,000 credits · ~1.5–3.0 kWh · ~0.9–12 L
A laptop for about a day
Why we’re publishing this
Aventide runs on leading AI models, Claude today, with others like Gemini planned as we grow, to think through your business questions and generate your documents. Every AI interaction uses electricity and a little water for cooling, somewhere in a data center. We think small business owners deserve to know roughly how much, in plain terms, not buried in a footnote and not oversold either way.
The honest catch: the AI providers we use don’t all publish exact energy or water figures. Google has, for Gemini. Anthropic, whose Claude model powers Aventide today, hasn’t published its own numbers yet. So the figures below are our best estimate, built from public industry research and our own pricing math, clearly labeled as an estimate, not a measurement. We’ll update this page as our model mix changes and as providers publish harder data.
What this looks like at Aventide’s usage levels
Every action in Aventide, a question you ask or a document you generate, uses a small number of credits, and credits map to a real, small amount of compute. Here’s what that adds up to if you used your entire monthly plan in a month. Most people use far less.
For context: a typical U.S. household uses about 30 kWh per day. Even a maxed-out Aventide plan, used at full capacity every day of the month, is a small fraction of that. Most Aventide customers use well under their full monthly allowance, so real-world usage is typically a fraction of the table above.
Per-interaction, roughly
A typical question or conversation: roughly 1–2 Wh, in the same ballpark as a single Google Gemini or ChatGPT query.
A typical generated document: roughly 2–3 Wh. It’s a bit more, since it’s a longer, more detailed output.
Water: somewhere between a few drops and about a tablespoon, depending on the data center’s cooling setup.
How we calculated this
We don’t have every provider’s real per-query energy numbers, so we built an estimate from what is public: Aventide’s own credit pricing, published AI model token pricing, and independent research on frontier-model inference efficiency. For water, we show a range bounded by Google’s disclosed water intensity and UC Riverside’s average-data-center estimate.
What we do to keep the footprint small
We choose AI models sized to the task rather than defaulting to the biggest, most compute-heavy model available.
We don’t store video files after they’re published. We keep only the thumbnail, metadata, and link.
Our infrastructure scales down to near-zero when you’re not using it. No AI model sits on waiting for you.
Sources
Google, “Measuring the environmental impact of delivering AI at Google Scale” (Aug 2025): arxiv.org/abs/2508.15734
Google Gemini energy disclosure coverage (Aug 2025): technologyreview.com/2025/08/21/1122288/google-gemini-ai-energy/
Mistral AI, “Our contribution to a global environmental standard for AI” (July 2025).
Ren et al., UC Riverside, “Making AI Less Thirsty” (2023): arxiv.org/abs/2304.03271
Epoch AI, “How much energy does ChatGPT use?”: epoch.ai/gradient-updates/how-much-energy-does-chatgpt-use
As our AI provider mix expands, we’ll update these figures and link directly to any provider’s own disclosure the moment one exists.
Why we’re publishing this
Aventide runs on leading AI models, Claude today, with others like Gemini planned as we grow, to think through your business questions and generate your documents. Every AI interaction uses electricity and a little water for cooling, somewhere in a data center. We think small business owners deserve to know roughly how much, in plain terms, not buried in a footnote and not oversold either way.
The honest catch: the AI providers we use don’t all publish exact energy or water figures. Google has, for Gemini. Anthropic, whose Claude model powers Aventide today, hasn’t published its own numbers yet. So the figures below are our best estimate, built from public industry research and our own pricing math, clearly labeled as an estimate, not a measurement. We’ll update this page as our model mix changes and as providers publish harder data.
What this looks like at Aventide’s usage levels
Every action in Aventide, a question you ask or a document you generate, uses a small number of credits, and credits map to a real, small amount of compute. Here’s what that adds up to if you used your entire monthly plan in a month. Most people use far less.
For context: a typical U.S. household uses about 30 kWh per day. Even a maxed-out Aventide plan, used at full capacity every day of the month, is a small fraction of that. Most Aventide customers use well under their full monthly allowance, so real-world usage is typically a fraction of the table above.
Per-interaction, roughly
A typical question or conversation: roughly 1–2 Wh, in the same ballpark as a single Google Gemini or ChatGPT query.
A typical generated document: roughly 2–3 Wh. It’s a bit more, since it’s a longer, more detailed output.
Water: somewhere between a few drops and about a tablespoon, depending on the data center’s cooling setup.
How we calculated this
We don’t have every provider’s real per-query energy numbers, so we built an estimate from what is public: Aventide’s own credit pricing, published AI model token pricing, and independent research on frontier-model inference efficiency. For water, we show a range bounded by Google’s disclosed water intensity and UC Riverside’s average-data-center estimate.
What we do to keep the footprint small
We choose AI models sized to the task rather than defaulting to the biggest, most compute-heavy model available.
We don’t store video files after they’re published. We keep only the thumbnail, metadata, and link.
Our infrastructure scales down to near-zero when you’re not using it. No AI model sits on waiting for you.
Sources
Google, “Measuring the environmental impact of delivering AI at Google Scale” (Aug 2025): arxiv.org/abs/2508.15734
Google Gemini energy disclosure coverage (Aug 2025): technologyreview.com/2025/08/21/1122288/google-gemini-ai-energy/
Mistral AI, “Our contribution to a global environmental standard for AI” (July 2025).
Ren et al., UC Riverside, “Making AI Less Thirsty” (2023): arxiv.org/abs/2304.03271
Epoch AI, “How much energy does ChatGPT use?”: epoch.ai/gradient-updates/how-much-energy-does-chatgpt-use
As our AI provider mix expands, we’ll update these figures and link directly to any provider’s own disclosure the moment one exists.