Strategic AI Infrastructure: Balancing Cost, Performance, and Governance

Enterprise infrastructure leaders are facing a critical strategic question: where should artificial intelligence (AI) run? This isn’t just an architectural decision; it impacts cost, risk, performance, data sovereignty, and competitive advantage.

Datacom experts—Matt Neil (Director – Data Centres), Mike Walls (Director – Cloud), and Daniel Bowbyes (Associate Director – Strategy)—share insights on designing right-sized AI infrastructure. They emphasize that AI isn’t a one-time deployment but rather a distributed capability requiring ongoing strategic decisions.

The Strategic Shift in AI Infrastructure

AI workloads demand more from infrastructure than traditional IT applications. High-density GPU environments can require 50–100 kW of power per rack, pushing facilities beyond standard capabilities. Without purpose-built data centres with advanced cooling, organizations increase operational and resilience risks.

The choice between on-premise, colocation, private cloud, or public cloud impacts:

  • Unit cost
  • Scalability
  • Resilience
  • Time to value
  • Control over critical workloads

Moreover, different AI functions—training, inference, generative AI—have varying requirements for architecture, connectivity, storage, and latency.

Key Considerations for Infrastructure Leaders

When deciding where AI should run, organizations must weigh:

  • Location of data and core systems
  • Data sovereignty and residency regulations
  • Intellectual property protection needs
  • Physical hosting capacity (power & cooling)
  • Latency/performance requirements
  • Vendor lock-in risks
  • Cost vs. value trade-offs (inference economics)

Right-Sizing AI Infrastructure

Instead of a one-size-fits-all approach, the right solution aligns infrastructure with specific use cases and organizational maturity.

For example:

  • High-performance training might benefit from on-premise or private cloud
  • Real-time inference applications require low latency
  • Data sovereignty concerns may favor local deployments

Datacom’s approach involves mapping AI activities to the optimal environment based on technical, governance, and business considerations.

As AI becomes embedded in core processes and customer experiences, organizations need confidence that data, models, and outcomes remain secure, compliant, and aligned with their strategic goals.