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Same compute. Smaller bill. Lower carbon.

Efficiency programs for data centers, cloud and AI workloads, from server utilization to cooling to carbon-aware scheduling.

Let's Build Together

One call with a senior engineer. A straight answer on what it would take.

Illustration: Data center energy efficiency

Where you are. Where you’ll be.

You need this if

  • Your data center is near its power limit
  • AI workloads are driving energy costs up fast
  • You can't report IT emissions with confidence

What changes for your business

  • Lower energy cost per unit of compute
  • Reduced emissions from IT operations
  • Headroom for AI growth without new power capacity

What we hand over

  1. Energy and utilization baseline across estate
  2. Consolidation, rightsizing and decommissioning
  3. Cooling and power-management optimization
  4. Carbon-aware workload scheduling

What it is

Data center energy efficiency means doing the same computing with less power: higher server utilization, right-sized infrastructure, better cooling, and moving flexible workloads to cleaner, cheaper hours. AI has made it urgent, because GPU clusters draw far more power than the servers many facilities were designed for.

Average server utilization is often low, and AI clusters sit idle between jobs. We measure energy and utilization across on-premises and cloud estates, consolidate and rightsize, tune cooling and power settings, and introduce carbon-aware scheduling that shifts flexible workloads to cleaner, cheaper hours and regions. Savings show up on the energy bill and the emissions report together.

Why now
2x data-center electricity demand by 2030, led by AI-optimized servers. Gartner, 2025
Last reviewed

How it runs

  1. 01

    Diagnose

    Typically 2–4 weeks

    We map the problem, your data and your systems, and agree the one number that defines success.

  2. 02

    Prove

    Typically 4–8 weeks

    A working pilot on your real data, measured against that number. Not a slide demo.

  3. 03

    Ship

    Scoped to the outcome

    Production build with security, monitoring, cost controls and documentation included, not upsold.

  4. 04

    Run

    Ongoing, optional

    We operate what we built against clear service levels, or train your team to. Your call. No lock-in.

Questions you’ll ask

Where does the energy waste usually come from?
Underused servers, idle GPU clusters between jobs, cooling set more conservatively than needed, and workloads running at fixed times regardless of energy price. Measurement usually reveals more idle capacity than anyone expected.
What is carbon-aware scheduling?
Running flexible workloads, such as batch jobs, model training and backups, when and where electricity is cleaner or cheaper. The work still gets done; it just moves to a better hour or region.
Why is this getting more urgent?
AI workloads are driving data-center electricity demand up fast; Gartner expects it to double by 2030. Many sites are already near their power limits, so efficiency is now a capacity question as well as a cost one.

Sound familiar? Let’s fix it.

One call with a senior engineer. You’ll leave with a straight answer on what it would take.

Let's Build Together

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