Colocation Lease
Definition
A colocation lease is a data-center arrangement where a customer places its own servers, networking equipment, or GPU infrastructure inside a third-party facility and pays for capacity, power, cooling, security, and connectivity. In AI infrastructure, colocation terms increasingly revolve around power availability, rack density, cooling design, uptime, and expansion rights.
Why it matters
Colocation leases turn physical infrastructure into recurring revenue, but the economic quality depends on contract structure. A lease priced by power, rack, square foot, or reserved capacity can produce different margins and risk allocation. For AI workloads, investors should examine whether the lease supports high-density GPUs, liquid cooling, power pass-throughs, and customer commitments long enough to justify capex.
Common misconceptions
- •Colocation is not the same as cloud computing; the customer may own or control much of the hardware.
- •A signed lease does not guarantee AI-ready economics if power delivery or cooling upgrades are not complete.
- •Data-center revenue can be constrained by power and cooling before square footage is fully used.
- •A long lease is not automatically strong collateral when the tenant has broad termination rights, limited guarantees, ramped capacity, or service-level remedies.
Technical details
Common pricing structures
Contracts may price by committed power capacity, racks, cages, cabinets, cross-connects, managed services, or metered usage. AI colocation often centers on power density and cooling capability rather than generic floor space.
Power costs can be fixed, passed through, indexed, or subject to separate power purchase arrangements. The allocation of power-price volatility matters for margin stability.
Key contract terms
Important terms include committed capacity, term length, renewal options, expansion rights, service-level agreements, uptime credits, power delivery, cooling specifications, security, remote hands, cross-connect pricing, and termination rights.
AI tenants may require specialized networking, liquid cooling support, and rapid deployment schedules that exceed standard enterprise colocation requirements.
Diligence questions
Is revenue tied to take-or-pay capacity, actual usage, or month-to-month occupancy?
Who pays for power volatility, cooling retrofits, and hardware refresh-related changes?
Can the facility support the tenant's expected rack density over the full lease term?
Revenue normalization
Separate base rent, power reimbursement, variable usage, installation revenue, credits, free periods, and optional expansion. Build monthly contracted, delivered, billed, and collected capacity.
Do not capitalize pass-through electricity as if it carried the same margin as infrastructure revenue.
Credit and asset matching
Underwrite the tenant, guarantor, deposit, renewal, termination, assignment, and bankruptcy provisions alongside specialized facility investment.
Stress tenant default, re-leasing downtime, retrofit cost, market rent, power availability, and whether replacements can use the same cooling and network design.
Capacity-to-revenue bridge
For Colocation Lease, bridge physical capacity to billable revenue. Start with contracted or announced units, then deduct capacity not yet delivered, powered, cooled, networked, commissioned, accepted by customers, or available after redundancy and maintenance requirements.
Build a monthly schedule for installed capacity, usable capacity, committed capacity, billed capacity, and collected revenue. This prevents double-counting the same GPU, rack, or megawatt across marketing pipeline, financing collateral, and customer backlog.
Separate high-margin infrastructure revenue from pass-through power, setup fees, burst usage, credits, taxes, and reimbursed costs. Revenue quality depends on margin, duration, collectability, and renewal probability, not only gross contract value.
Contract and counterparty diligence
Review the exact contracting party, guarantor, minimum commitment, ramp schedule, delivery conditions, service levels, termination rights, cure periods, force majeure, assignment rights, deposits, and lender step-in rights.
Customer quality matters because AI demand can be volatile. Underwrite concentration, funding runway, payment history, use case, workload portability, and whether the customer can switch to hyperscalers or newer hardware.
Supplier diligence should cover title transfer, liens, serial-number evidence, warranty, replacement rights, export controls, delivery delay remedies, and whether a reseller actually controls the inventory it promises.
Operating constraints and cost stack
AI compute economics are constrained by power price, power availability, cooling design, rack density, network fabric, facility uptime, maintenance, software orchestration, spare parts, and labor. A GPU fleet can be technically installed but commercially weak if one of these constraints binds.
Stress power-price increases, curtailment, delayed interconnection, transformer lead times, cooling retrofits, customer credits, lower utilization, and hardware failures. Compare gross utilization with contribution margin after power and operating costs.
For financing, match customer contract tenor and hardware useful life to debt amortization. A long loan against short-lived or rapidly repricing hardware can leave residual-value risk with the lender or vehicle.
Refresh, residual value, and monitoring
Track hardware by cohort: model, purchase date, installed cost, memory profile, networking, warranty, utilization, average realized rate, power draw, and expected resale or redeployment value.
Monitor competitive GPU pricing, new chip launches, customer workload shifts, inference versus training mix, cloud spot pricing, and resale market depth. A unit that still functions can become economically stale before physical failure.
Warning signs include revenue booked before acceptance, unclear ownership of hardware, repeated delivery delays, rising service credits, power constraints, low realized utilization, customer nonpayment, and capex needs that are not reflected in the financing model.