The Old Playbook Is Broken
Enterprise colocation procurement has traditionally focused on three variables: location, price, and SLA. You pick a market close to your users, negotiate the best rate per kW, and ensure the provider meets your uptime requirements.
For AI workloads, this approach fails on multiple dimensions.
What Makes AI Different
AI and machine learning infrastructure has fundamentally different requirements:
- Power density — a single AI training rack can draw 40–80kW, versus 8–15kW for traditional enterprise workloads
- Cooling requirements — air cooling alone can’t handle modern GPU densities; liquid cooling is increasingly required
- Network architecture — AI clusters need high-bandwidth, low-latency interconnects between nodes
- Scale trajectory — AI deployments grow faster than any other workload category, requiring built-in expansion capacity
The Strategic Framework
An effective AI colocation strategy addresses four key areas:
Power Planning
- Size your power requirements for 3–5 years out, not just current needs
- Negotiate density escalation clauses that allow you to increase per-rack power without renegotiating the entire contract
- Understand the facility’s utility relationship — can they deliver incremental power as you scale?
Cooling Architecture
- Evaluate the provider’s liquid cooling capabilities and roadmap
- Understand the cooling efficiency metrics (PUE) at your target density
- Assess whether the facility can retrofit existing space for high-density cooling
Market Selection
- Consider Tier-2 markets where power costs are lower and availability is greater
- Evaluate multiple markets to create optionality and negotiating leverage
- Factor in talent availability for on-site operations staff
Contract Structure
- Negotiate flexible terms that accommodate rapid growth
- Include provisions for technology refresh and density increases
- Consider multi-market deployments to manage risk and optimize cost
Common Pitfalls
Enterprises consistently make these mistakes when deploying AI infrastructure:
- Under-provisioning power — starting with “just enough” and hitting the wall in 12 months
- Single-market concentration — putting all AI capacity in one facility or market
- Ignoring liquid cooling — assuming air cooling will handle next-generation hardware
- Short-term contracts — signing 1–2 year deals instead of locking in favorable rates for 3–5 years
The Path Forward
Building an AI-ready colocation strategy requires thinking differently about procurement, market selection, and contract structure. The enterprises that get this right will have a meaningful infrastructure advantage over competitors who are still running the old playbook.
In AI infrastructure, the cost of being late is measured in months of lost training time and competitive position.