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World's First Carbon-Aware AI Router

Environmental Impact Tracking & Smart Routing

Every AI request has a carbon footprint. ModelPilot is the world's first AI router that tracks, optimizes, and reduces environmental impact alongside cost and performance.

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The Hidden Environmental Cost of AI

High Energy Use
Large AI models consume massive amounts of electricity for training and inference, contributing significantly to carbon emissions.
Regional Variance
Data center locations matter - the same model can have vastly different carbon footprints depending on the energy grid.
Lack of Visibility
Most developers have no insight into the environmental impact of their AI usage, making optimization impossible.

How ModelPilot Reduces AI Carbon Footprint

We combine real-time tracking, intelligent routing, and sustainability reporting to help you build greener AI applications.

Real-Time CO₂e Tracking

Every API request includes detailed carbon footprint metrics calculated from:

  • Model size and architecture efficiency
  • Provider data center locations and energy sources
  • Request complexity and token usage
  • Regional electricity carbon intensity

Track your cumulative environmental impact in real-time dashboards.

Carbon-Aware Routing

Our smart router automatically optimizes for environmental impact:

  • Route to energy-efficient model architectures
  • Prioritize providers with renewable energy
  • Balance carbon footprint with quality requirements
  • Avoid unnecessarily large models for simple tasks

Configure environmental impact weight in your router settings.

Sustainability Reports

Demonstrate environmental responsibility to stakeholders:

  • Monthly carbon impact summaries
  • Comparative analysis against industry baselines
  • Emissions reduction tracking over time
  • Exportable reports for ESG compliance

Perfect for corporate sustainability initiatives and reporting.

Actionable Insights

Get AI-powered recommendations to reduce your footprint:

  • Identify high-carbon usage patterns
  • Suggest more efficient model alternatives
  • Batch optimization opportunities
  • Best practices for sustainable AI development

Continuous optimization suggestions based on your usage patterns.

Why Environmental Optimization Matters

50%+
Potential carbon reduction by routing to efficient models
10x
Carbon difference between energy sources by region
100%
Transparency into every request's environmental cost

How We Calculate Carbon Footprint

1Model Energy Consumption

We estimate energy usage based on model parameters, architecture efficiency (dense vs. sparse/MoE), and token throughput. Larger models consume more energy per token.

2Provider Infrastructure

We track which provider and data center region is serving your request, accounting for their energy sources and infrastructure efficiency.

3Regional Carbon Intensity

Using real-time carbon intensity data from electricity grids, we convert energy consumption to CO₂e emissions. A model running in Iceland (geothermal) has far lower emissions than the same model in coal-heavy regions.

4Request-Level Attribution

Every API response includes precise CO₂e metrics, allowing you to track cumulative impact and optimize high-usage endpoints.

Start Building Sustainable AI Today

Join forward-thinking developers who are reducing their AI carbon footprint without compromising on performance or cost.

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No credit card required • Track impact from day one