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Neither colocation nor building your own data center is always cheaper or faster for AI. Colocation can reduce upfront facility investment and shift facility operations to a provider; building can offer more control and may lower long-run total cost when utilization is sustained. The deciding factors are whether suitable power and cooling can be delivered on schedule, how much capacity you will use, and whether you can fund and operate the facility. Compare both options against the same workload, location, schedule, service level and time horizon.
What changes when you choose colocation or build?
With colocation, a provider supplies facility infrastructure—typically space, power, cooling and connectivity—while you own and operate the IT equipment placed there. With a self-built data center, your organization takes responsibility for developing and operating the facility as well as managing its IT estate. This is a distinction in both capital allocation and operational responsibility, not simply a choice between two buildings.
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| Decision area | Colocation | Build your own |
|---|---|---|
| Facility infrastructure | Provider supplies the contracted facility services and capacity. | Your organization funds, develops and operates the facility. |
| IT equipment | Tenant generally supplies and operates its own servers and other IT equipment; facility pricing should not be mistaken for an IT hardware budget. | Your organization supplies and operates the IT estate as well as the facility. |
| Upfront capital | Can reduce initial facility capital requirements, though equipment, deployment and contract costs remain. | Requires substantial facility capital, in addition to IT investment. |
| Operational responsibility | Provider operates the contracted facility systems; tenant remains responsible for its IT and for confirming the service meets workload requirements. | Owner must have the people and processes to operate and maintain facility systems alongside IT. |
| Control and flexibility | Capacity, design choices and expansion depend on available provider offerings and contract terms. | Offers greater control over facility design and operation, within site, utility, permitting and budget constraints. |
How do AI workloads affect the facility decision?
“AI workload” does not describe one fixed power or cooling requirement. Large-scale model training and advanced inference may call for high-density racks, upgraded power delivery, liquid cooling, adequate structural capacity and close coordination between IT and facilities teams. Other AI workloads may not need all of those features. Size the facility around the actual accelerator configuration, workload profile and deployment plan—not the label “AI.”
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Define the workload before requesting capacity
- Separate training from inference and describe expected load patterns, including peaks and ramp-up.
- Specify accelerator count and generation, expected rack density, and the networking and latency requirements for the workload.
- Estimate the capacity needed at launch and how it will grow, rather than treating the eventual maximum as the initial requirement.
- Set availability and service-level requirements, including the consequences of facility or power interruptions.
Cooling is part of the power and cost model, not a minor add-on. The International Energy Agency’s 2025 Energy and AI report says cooling ranges from about 7% of electricity use in efficient hyperscale data centers to more than 30% in less-efficient enterprise data centers. That spread reflects different facility types and efficiencies; it is not a design allowance for a particular project.
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Is it cheaper to build a data center or use colocation?
There is no universal break-even workload size or ownership period established by the available evidence. Schneider Electric’s vendor-authored guidance describes a common trade-off: owning typically has lower long-run total cost of ownership (TCO) but requires substantial capital and periodic reinvestment, while outsourcing can reduce initial capital needs yet accumulate higher costs over a five-to-ten-year horizon. Treat that as a framework, not a forecast for your project.
Make the comparison on equivalent cost boundaries. McKinsey Global Institute’s 2026 modeled colocation economics explicitly exclude customer-owned IT hardware; comparing that facility cost with a build estimate that includes IT equipment would give a misleading result. Include the same categories on both sides:
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- Facility development or colocation charges, including contracted power and cooling services.
- IT hardware and its replacement or upgrade cycle.
- Energy, utility charges and the cost of securing usable power.
- Financing and cost of capital, facility staffing, maintenance and operational support.
- Permitting, taxes or incentives, and the cost of upgrades needed for the target density or cooling method.
- Residual value, unused capacity and the risk that an asset or contracted capacity is stranded.
Run the comparison over the same five-to-ten-year horizon and test several utilization and power-price cases. Include a delayed deployment case: a facility that is nominally less expensive but not energized when the workload is ready can carry a substantial business cost. Use local provider proposals and site-specific engineering estimates rather than a global benchmark.
Why power access and delivery dates can outweigh nominal facility cost
Power availability, location and schedule are economic inputs. A site or provider needs not only sufficient eventual capacity, but a credible path to delivering the required megawatts on the workload’s timeline. Grid interconnection delays and equipment lead times can constrain either a new build or an expansion. Confirm utility capacity and dates, permitting, equipment availability and provider delivery commitments before treating a schedule as firm.
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Location also affects energy cost and workload fit. McKinsey Global Institute’s 2026 model for a 100 MW Tier 3-equivalent AI colocation facility, excluding IT hardware, estimated levelized facility-energy costs from roughly $200/MWh in some high-demand Chinese markets to close to $380/MWh in London. These are model-specific, pretax results under stated assumptions—not retail electricity tariffs, local quotes or a universal comparison of building and leasing.
Global demand figures provide context, not a sizing shortcut. The International Energy Agency estimated data centers used around 415 TWh, or about 1.5% of global electricity consumption, in 2024 and projected roughly 945 TWh by 2030 in its base case. Those figures cover data centers globally, not AI alone, and do not predict the power requirements of an individual facility.
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What does the colocation market evidence tell you?
Colocation is used for hyperscale capacity, but market indicators are not a substitute for a project quote. Uptime Institute’s 2025 survey found that 62% of surveyed colocation facilities hosted hyperscale technology companies; that is a survey result, not a claim about the share of every facility worldwide.
Pricing can also move with market conditions. CBRE Research reported that primary North American wholesale colocation asking rates for 250–500 kW rose 6.6% year over year to $196.25 per kW per month in H2 2025. For 3–10 MW, asking rates rose 12.5% year over year. These are geographically and capacity-scoped asking-rate indicators, not guaranteed rates for a particular location, AI-ready configuration, contract or delivery date.
When does each option fit?
Colocation may fit when
- You need to limit upfront facility capital or want to avoid taking on facility development and operations.
- A provider can demonstrate suitable power density, cooling, redundancy, connectivity and capacity on your deployment schedule.
- Your demand or location needs remain uncertain enough that a contract-based capacity path is preferable to owning a fixed facility.
- The provider’s contract terms, expansion options and service commitments match the expected workload lifecycle.
Building may fit when
- You expect sustained, high utilization over a long horizon and the modeled savings justify the capital and ongoing operating responsibility.
- You need facility design or operational control that available colocation offerings cannot meet.
- You can secure a suitable site, power, permits, engineering, capital and a qualified operations team on schedule.
- You can plan for periodic reinvestment, maintenance, upgrades and capacity changes rather than evaluating only initial construction cost.
A retrofit may be a third option
An existing facility can be worth assessing when it has enough space, power, cooling potential and structural capacity for the intended deployment. A retrofit is not automatically faster or cheaper: engineering must establish whether the site can support the required density, cooling method, redundancy and delivery schedule.
How to make a defensible build-versus-colocation decision
- Characterize the workload. Document training versus inference, accelerator count and generation, power profile, network and latency needs, growth forecast and service-level requirements.
- Set the facility envelope. Estimate launch and ramp-up megawatts, rack density, cooling approach, redundancy, fiber needs and site constraints.
- Verify deliverability. Obtain evidence for utility capacity and interconnection dates, permits, equipment lead times, and provider or construction delivery commitments.
- Normalize the costs. Compare facility capital or colocation charges, IT hardware, energy, financing, staffing, maintenance, upgrades, taxes or incentives, and residual or stranded-asset risk using the same boundaries.
- Stress-test the model. Compare several utilization, power-price and deployment-delay cases over an identical time horizon. Make assumptions explicit and check which ones change the preferred option.
- Assess a retrofit alongside both choices. Include it only if a site-specific assessment can verify adequate space, power, cooling and structural integrity.
There is no project-specific answer without local power commitments, workload and utilization forecasts, engineering estimates, financing terms and provider proposals. The useful outcome is not a generic claim that ownership or colocation always wins; it is a comparison whose assumptions match the workload and can be tested against actual delivery commitments.
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