Behind-the-Meter Power: Reducing AI Infrastructure Costs

The Power Problem in AI

AI inference is energy-intensive. A single large language model query can consume 10× the energy of a traditional web search. At scale, power becomes one of the largest operational costs—and a significant sustainability concern.

What is Behind-the-Meter Power?

Behind-the-meter (BTM) refers to electricity generation that connects directly to a facility, bypassing the public grid. For AI datacentres, this typically means:

• On-site solar or wind generation
• Direct power purchase agreements (PPAs) with adjacent renewable facilities
• Co-location with power generation assets

The Economics

Traditional grid power for datacentres includes:

• Generation costs
• Transmission and distribution fees
• Grid maintenance charges
• Regulatory levies

Behind-the-meter arrangements eliminate most of these intermediary costs. Our analysis shows:

60% Cost Reduction

Direct renewable integration reduces power costs from typical grid rates to near-generation costs. For a facility running 24/7 inference workloads, this compounds dramatically.

Price Stability

Long-term PPAs lock in energy costs, protecting against:

• Wholesale market volatility
• Carbon pricing increases
• Grid infrastructure charges

Carbon Neutrality

Direct renewable sourcing provides genuine carbon reduction, not just offset certificates.

Implementation Considerations

Location Selection

BTM viability depends heavily on geography:

• Solar: Optimal in high-irradiance regions
• Wind: Requires consistent wind resources
• Hydro: Limited to specific locations but highly reliable

Capacity Matching

AI workloads have variable demand. Successful BTM implementations require:

• Battery storage for load smoothing
• Grid interconnection for backup
• Workload scheduling aligned to generation patterns

Regulatory Navigation

Power arrangements vary by jurisdiction. Key considerations:

• Wheeling agreements for nearby generation
• Grid connection requirements
• Licensing for self-generation

Case Study: Renewable-Aligned Scheduling

Our infrastructure uses intelligent workload scheduling to maximise renewable utilisation:

1. Batch inference jobs shift to peak solar/wind periods
2. Real-time inference maintains priority with battery buffer
3. Model updates and maintenance schedule during surplus generation

This approach achieves 85%+ renewable utilisation without compromising latency SLAs.

The Broader Impact

Beyond cost savings, BTM power addresses:

• Scope 2 emissions for enterprise customers
• ESG reporting requirements
• Regulatory pressure on datacentre energy consumption

Conclusion

Behind-the-meter power isn't just about cost reduction—it's about building AI infrastructure that scales sustainably. As energy becomes an increasingly large share of inference costs, power strategy becomes competitive advantage.

Contact [email protected] to learn more about our energy-efficient infrastructure.