The price you pay for AI is increasingly tied to the price and location of power. As energy grids tighten, the true cost of AI is no longer measured in tokens and instances. It is measured in validated answers, delivered at a specific carbon intensity and latency threshold.

If your contracts don’t account for energy availability, pricing volatility, and environmental impact, you’re negotiating with yesterday’s playbook.

Why Power Now Sits Inside Your AI Commercials

1. Energy volatility has moved off the cloud bill: Regional tariffs, transmission congestion, water constraints, and PUE (Power Usage Effectiveness) now swing total cost more than list prices. These factors are dynamic, location-specific, and often unpredictable.
2. Capacity is being triaged: Providers are prioritising workloads that come with energy underwriting, such as PPAs, renewable adders, and predictable load shapes. In constrained metros, energy-backed deals get capacity first.
3. Power is now a pricing lever: Buyers who treat power as a first-class commercial term—co-pricing compute with kilowatts—gain predictability, sustainability credibility, and priority access when grids are stressed. Those who separate compute and facilities lose leverage and face higher costs.

Avasant’s Compute – Power Contract Stack

Understanding the cost dynamics at each layer of the compute stack is crucial for organisations aiming to achieve both economic efficiency and sustainability. The AI Total Cost of Ownership (TCO) model shows that Layer 1 (Energy) now drives the greatest cost variability. Organisations can transform energy volatility into strategic levers by co-pricing compute with kilowatts. As you move up the stack—from Hardware and Platform to Model and Application layers—contractual moves like evergreen refreshes and throughput guarantees compound the benefits. Table 1 outlines how energy terms should be embedded across each layer to unlock cost predictability, performance stability, and sustainability impact.

The goal of this framework is simple but powerful: bring energy terms forward in your negotiations so that optimisations at higher layers—hardware, platform, model, and application—can deliver real, measurable traction. By embedding energy-aware clauses early, you unlock compounding benefits across the stack: cost predictability, performance stability, and sustainability gains that directly support business outcomes.

Negotiation Moves You Can Use Now

As AI infrastructure becomes increasingly dependent on energy availability and pricing, sourcing leaders must evolve their contract strategies to reflect this new reality. Table 2 outlines five high-impact negotiation moves designed to help you secure predictable costs, improve sustainability, and maintain operational flexibility. For each move, we’ve summarised what to ask for, why it matters, and the business outcome you can expect.

AI Energy-Contract Readiness: What to Do This Month

To future-proof your AI infrastructure and sourcing strategy, take the following steps this month:

1. Baseline Your Energy Exposure by Region

• • Identify where your current and planned AI workloads are running.
  • For each region, gather data on:

1. 1. 1. Tariffs: Understand local electricity pricing structures.
      2. Carbon Mix: Assess the percentage of renewable vs. fossil fuel energy.
      3. PUE (Power Usage Effectiveness): Evaluate data center efficiency.

This baseline will help you assess cost risk, sustainability impact, and sourcing leverage.

2. Segment Steady vs. Spiky AI Loads

Once you’ve established your regional energy exposure, the next step is to classify your AI workloads based on how they consume compute and power:

• • Steady Loads are consistent, predictable workloads such as customer service bots, recommendation engines, or analytics pipelines. These are ideal for long-term energy contracts like PPAs or virtual PPAs, which offer stable pricing and cleaner energy sourcing.
  • Spiky Loads are irregular or bursty workloads that fluctuate in demand—like model training, seasonal campaigns, or experimentation environments. They are better suited to flexible, indexed pricing models that allow you to scale without overcommitting.

Matching the right energy strategy to each workload helps you optimize cost, maintain flexibility, and support sustainability goals without compromising performance.

3. Draft Energy-Indexed Pricing and PUE Clauses

• • Collaborate with Legal and ESG teams to:

1. 1. 1. Define pricing tied to regional energy indices.
      2. Set PUE caps and efficiency incentives.
      3. Include carbon-intensity riders and curtailment relief terms.

These clauses will help you manage cost volatility and align with sustainability goals.

4. Run a Cloud vs. Colo vs. On-Prem Bake-Off

• • Evaluate deployment models using energy as a scoring dimension:

1. 1. 1. Cloud: Flexible but may lack transparency on energy sourcing.
      2. Colo (colocation): Offers control over energy contracts and location.
      3. On-Prem: High control, but potentially higher upfront costs.

Score each option based on unit cost, carbon intensity, and latency to guide infrastructure decisions.

5. Build an Executive Dashboard

Create a simple, visual dashboard with three key metrics by region:

• • Unit Cost: $ per validated answer or inference.
  • Carbon Intensity: gCO₂/kWh or renewable share.
  • Latency: Response time for critical workloads.

This dashboard supports strategic decision-making and board-level reporting.

Who Should Do What (Persona Playbook)

Successfully integrating energy terms into AI contracts requires cross-functional coordination. Each leadership role plays a distinct part in ensuring cost predictability, sustainability alignment, and operational flexibility. Table 3 outlines who owns what, so your organisation can move from reactive sourcing to strategic energy-backed AI procurement.

Sustainability & Board Messaging

Position energy-integrated AI sourcing as both a risk mitigation strategy and a brand-positive initiative:

• • Predictable Cost: Reduce exposure to energy price volatility.
  • Assured Capacity: Secure access during grid stress or regional constraints.
  • Measurable Carbon Reductions: Track and report improvements in carbon intensity.

Provide boards with location-specific disclosures on:

• • Water usage
  • Carbon emissions
  • Curtailment risks

This ensures ESG reporting is not only accurate but also aligned with commercial strategy—strengthening investor confidence and regulatory compliance.

Take the Next Step: Energy-Backed Capacity Review

Book Avasant’s 30-minute Energy-Backed Capacity Review to ensure your AI infrastructure is optimized for cost, sustainability, and resilience.

In this focused session, our experts will:

• • Diagnose your top regions for tariff, PUE, and carbon exposure.
  • Draft energy-indexed pricing, PUE-cap, and relocation-rights clauses tailored to your portfolio.
  • Model cost-per-validated-answer under multiple grid and efficiency scenarios—so you can choose the mix that balances cost, carbon, and latency.

Outcome: a ready-to-negotiate clause pack and an executive-level options memo you can use this quarter.

Whether you’re sourcing new capacity or renegotiating existing contracts, this review helps you future-proof your AI strategy and strengthen your commercial position.