Procurement Archetypes: Build vs Buy vs Rent

Chapter 1.1 established the first orthogonal question — what runs here. This chapter is the second — how you acquire the capacity to run it. The two are independent: a frontier pre-training run can be served from a self-build, a colo, or a neocloud, and an online-inference business can be any of the four. But the procurement decision has a character all its own. The workload question is answered once and is mostly about physics. The procurement question is answered repeatedly, is mostly about finance and time, and is the place where most operators in 2026 are actually getting hurt — not because they pick the wrong cooling, but because they commit capital to a demand forecast they could not honestly defend, or because they rent at a premium for years a load they should have owned.

The fork is build vs buy vs rent, across four paths: greenfield self-build, brownfield retrofit, colocation (wholesale or retail), and neocloud / GPU rental. We trace each against the four levers that actually decide it — time-to-power, capital intensity, control, and the workload-duration / demand-certainty axis that quietly dominates the other three. We then treat the two structural realities that reshape the whole decision in 2026: the power gate (grid interconnection vs behind-the-meter generation), and demand uncertainty, which converts the build-vs-lease choice from a cost comparison into an options problem. The quantitative NPV that scores these paths lives in Chapter 1.8; the deal mechanics and accounting in Chapter 2.5. This chapter is the strategy that sits above both.

Practitioners list four decision drivers — time-to-power, capital intensity, control, workload duration — as if they were peers. They are not. The first three are real constraints, but they are usually satisfiable in more than one way. The fourth — how long and how certainly the workload will run — is the lever that breaks ties and, more often than not, makes the decision outright. The reason is depreciation. An AI cluster is the rare capital asset whose economic life (2–3 years at the frontier) is shorter than the financing tenor used to buy it (5–6 year book life, longer debt). Commit to a self-build for a workload that evaporates in eighteen months and you own a stranded asset against a loan that outlives the revenue. Rent that same workload and you simply stop paying.

Time-to-power is the lever that has moved most since 2020, because the binding constraint is no longer chips or capital but megawatts. A neocloud delivers GPUs in minutes; a colo delivers a live cluster in 6–12 months; a self-build delivers in 24–36 months — and that self-build number is now gated less by construction than by the interconnection queue behind it, which can run 4–7 years in the densest US hubs. When time-to-power is the binding constraint, the procurement decision collapses toward whoever already holds energized capacity, almost regardless of unit cost. This is why neoclouds exist as a category and why colocation pricing hit record highs in 2025 at roughly ~1% vacancy: the scarce good is not space, it is power that is already on.

Capital intensity is the lever that decides who can play at all. A 1 GW self-build runs on the order of tens of billions up front; a colo lease converts most of that into a per-kW-month operating expense; a neocloud converts all of it into a per-GPU-hour rate. For a startup, an enterprise dabbling, or a sovereign program racing a political clock, the capex wall alone forecloses self-build. Control is the lever that pulls the other way: only a self-build (and to a bounded degree a retrofit) gives you authority over density, power architecture, cooling modality, fabric topology, and reliability posture — the very levers Chapter 1.1's cascade showed are load-bearing for a training-shaped facility. Rent, and you inherit the provider's fabric, oversubscription, and uptime — fine for inference, often disqualifying for a frontier training run that needs a 1:1 non-blocking back-end.

Greenfield self-build is the maximal-control, lowest-long-run-unit-cost, slowest, most capital-intensive path. You design the slab, the power chain, the cooling plant, and the fabric to your workload's exact cascade, and at scale and high utilization you reach the lowest cost per GPU-hour available — on the order of $0.74/GPU-hr for an H100-class cluster at 2,048-GPU scale and 90% utilization, against a neocloud median several times that. The price is 24–36 months to a live cluster, the deepest capital commitment, and an interconnection slot that is itself the scarcest asset in the project. Self-build is correct only when the demand is durable enough to amortize all of that — and disciplined enough to keep utilization high, because the entire unit-cost advantage evaporates below roughly 70% utilization, the breakeven where a debt-financed cluster flips from cash-positive to cash-negative.

Brownfield retrofit trades capital and schedule for a hard physics ceiling. Converting an existing air-cooled hall to host dense AI racks is fast and cheap relative to greenfield — roughly $2–3M/MW for the cooling upgrade alone, $5–10M/MW for a full AI retrofit — but the existing slab, plenum, electrical headroom, and (critically) the absence of facility water cap how far you can push density. A hall scoped for 40 kW air-cooled racks cannot absorb a 132 kW liquid-cooled NVL72 generation; an estimated two-thirds of pre-2015 data centers are simply unsuitable for frontier AI density without effectively rebuilding them. Retrofit is the right call for bridge capacity and for modest-density inference that lives comfortably under the air-cooling cliff — and the wrong call the moment your roadmap crosses into training or next-generation dense inference. → Chapter 5.4.

Colocation — wholesale (you lease a powered hall and own the IT) or retail (you rent cabinets and power by the rack) — buys time-to-power by renting someone else's energized shell. A live 50k+ GPU cluster can stand up in a wholesale hall in 6–12 months, against the 24–36 of a self-build, because the long-lead power and building work is already done. The cost is paid in a per-kW-month lease (global wholesale averaged roughly $217/kW-month in 2025, a record, ranging from ~$120 in Atlanta to ~$450 in Singapore) and in a shared shell whose redundancy tier and power envelope you accept rather than design. Colo is the natural home for medium-term, scaling, demand-uncertain workloads — and the anchor of most hybrid strategies.

Neocloud / GPU rental compresses time-to-first-job to days or weeks and converts capex entirely into opex, at the highest per-GPU-hour rate and the least control over the underlying fabric and reliability. Neocloud rates run 40–85% below the hyperscalers (an 8-GPU node averaging ~$34/hr on a neocloud vs ~$98/hr on a hyperscaler in 2026), which is what makes rental viable for spiky, short, experimental, or burst-overflow demand. But the category carries two 2026-specific cautions: pricing is volatile (the one-year H100 contract index rose ~40% between October 2025 and March 2026 as capacity tightened), and on-demand capacity has been effectively sold out across most GPU types with reserved capacity booked months ahead — so the "rent it instantly" promise is conditional on a market that periodically has nothing to rent.

Down the unit-cost column the temptation is to self-build everything. Down the time-to-power and demand-profile columns the discipline reasserts itself: the cheapest-per-hour option is also the slowest and the one that punishes a wrong demand forecast most severely. The correct reading is that these archetypes are not competitors for a single decision — they are complements layered against the shape of your demand curve. That is what the hybrid strategies operationalize.

In practice almost no serious operator picks one cell. The demand curve has a stable base and a volatile tail, and the right strategy matches each layer to the archetype whose cost structure fits it. Three patterns recur:

Build-core-rent-edge. Own the durable base load where you are most certain of demand and most need control (a self-built training core), and rent the geographically distributed inference tail from colos and neoclouds near users, where proximity matters more than unit cost and demand is harder to forecast site by site. This is the canonical hyperscaler and large-neocloud posture, and it maps cleanly onto Chapter 1.1's training-shaped vs inference-shaped fork: own the training-shaped, rent the inference-shaped.

Colo-anchor-plus-cloud-overflow. Anchor a committed, predictable load in a wholesale colo on a multi-year lease (capex-light, fast, controllable enough), and absorb peaks — the bursty 30%-to-90% swings an online-inference business sees in minutes — by bursting to a neocloud at the margin. You pay the rental premium only on the volatile fraction, not the whole fleet, which is the point: rental's high unit cost is acceptable precisely when it is applied to the part of demand you could not have committed to in advance.

Burst-to-neocloud (bridge and experiment). Run steady state on owned or colo capacity, and use neocloud rental as a bridge while a self-build energizes, as elastic capacity for a one-off training run or evaluation sweep, or as the proving ground for a workload whose durability you do not yet trust. The moment a rented workload proves durable and large, the hybrid logic says re-home it to owned capacity — the crossover point where owning beats renting is a function of utilization, depreciation schedule, and resale liquidity, and is treated quantitatively in Chapter 1.8.

Every procurement choice above assumes power exists to be procured. In 2026 that assumption is the project's most dangerous one. Time-to-power is set less by how fast you can build than by how fast you can energize, and energization runs through one of two gates — grid interconnection or behind-the-meter generation — whose lead times can dominate every other line in the schedule. A self-build that takes 24 months to construct but sits behind a 5-year interconnection queue is, functionally, a 5-year project. This is why the power gate belongs in the procurement chapter and not only in the power chapters: it can invert the entire build-vs-rent ranking.

Grid interconnection is the default and, increasingly, the bottleneck. US large-load interconnection runs roughly 3–7 years end-to-end and up to ~10 in the worst queues, against a national queue measured in thousands of gigawatts of requested capacity. The regulatory ground is moving fast: FERC's December 2025 PJM colocation order forced standardized service terms for loads co-located with generation, and DOE's late-2025 directive sought to federalize interconnection for loads above 20 MW — both aimed at the queue, both unresolved enough as of mid-2026 to be a planning risk rather than a planning certainty. The consequence for procurement is direct: if your candidate site's queue position is bad, colo or neocloud is not a fallback, it is the only path to revenue inside your depreciation window.

Behind-the-meter (BTM) generation — gas turbines, on-site generation, increasingly nuclear and SMR PPAs — is the escape hatch, and it has become a primary procurement gate in its own right. On-site gas can bring power in 18–36 months, faster than many grid queues, which is why announced BTM gas reached on the order of 82–101 GW cumulatively by 2026 (though only a few GW were actually online by mid-year — an announcement-stage figure, not a built one). Aeroderivative turbines, the fast option, now carry 18–36 month lead times themselves as orders surge. Choosing BTM buys speed-to-power and revenue — getting 200 MW online six months early is worth ~$1B at ~$10–12B/GW/yr of AI revenue — but trades grid economics, exposes you to fuel and emissions risk, and locks in generation assets with their own depreciation profile. → Chapter 3.2 (speed-to-power) and Chapter 3.1 (the reordered siting hierarchy).

Chapter 1.1 sorted the physical decisions by reversibility. The procurement stack has its own register, and it is where the demand-uncertainty hedge actually gets spent.

Irreversible (decide once, hedge now): the self-build slab and its interconnection slot; the take-or-pay power contract (a multi-year firm commitment to buy energy whether or not you use it); the long BTS lease; and the behind-the-meter generation assets, which carry their own depreciation and cannot be unwound cheaply. These are the decisions where a wrong demand forecast compounds — you pay for capacity you do not use against a contract you cannot exit.

Reversible (defer, keep cheap): the neocloud provider and the rental volume (switch or stop monthly); the colo expansion option (exercise it only when demand materializes); the burst-overflow relationship; and the IT fit-out generation within a powered shell. The strategic move, identical in spirit to Chapter 1.1's, is to convert irreversible commitments into reversible ones wherever the option premium is cheap: a powered shell instead of a BTS, a colo lease with expansion rights instead of a self-build, a burst-to-neocloud bridge instead of energizing owned capacity ahead of demand. You pay the rental and lease premiums precisely as insurance against the demand curve you assumed being wrong. → refresh and re-home execution in Chapter 14.9.

The archetype choice is not a single pick but a short ordered set of questions, each of which can foreclose later options:

• 1. How certain is the demand? Durable and contracted → own (self-build or BTS). Spiky, short, or a genuine bet → rent. Somewhere between → colo-anchor with rental overflow. This is the master fork and it gates everything below.
• 2. What is the time-to-power need? Revenue needed in weeks → neocloud. In months → colo. In years, and demand justifies the wait → self-build. If the need is fast but the demand is durable, bridge with rental while a build energizes.
• 3. What does the workload demand in control? Frontier training needing a 1:1 non-blocking fabric and a specific density/cooling cascade → own or wholesale colo. Inference that fits a node → any path, including rental.
• 4. What is the power gate? A bad interconnection queue can convert "self-build" into "colo or neocloud by necessity," or push you toward a behind-the-meter path with its own lead time and economics. Resolve this before committing to any owned path.
• 5. Which commitments are you about to make irreversible? Tag every take-or-pay, lease term, slab, and generation asset, and ask whether a powered shell, an expansion option, or a rental bridge could keep it reversible at an acceptable premium.

The output of this sequence is the procurement layer of the design-basis document from Chapter 1.1: which load is owned, which is leased, which is rented, what the power gate is, and which commitments are hedged versus fixed — signed before any long-lead equipment or power contract is ordered.

Each recurring procurement mistake comes from optimizing one lever in isolation and ignoring the demand-certainty axis that should have governed it:

• Building against an assumed demand curve. Self-building (or signing a long take-or-pay) for a workload whose durability was assumed, not contracted. When the curve disappoints, you own a stranded asset that runs cash-negative below ~70% utilization against debt that outlives the revenue. The fix is to rent or colo the uncertain fraction until it proves durable.
• Renting a certainty. Keeping a large, durable, high-utilization base load on neocloud for years out of inertia, paying a 40–85% margin repeatedly on compute you should own. The fix is the own-vs-rent crossover re-test in Chapter 1.8.
• Ignoring the power gate. Selecting a self-build site on land and construction economics while the interconnection queue behind it quietly makes the project a 5–7 year one — missing the depreciation window entirely. The fix is to treat queue position and the BTM alternative as a first-order procurement input, not a later power-engineering detail.
• Retrofitting past the air-cooling cliff. The procurement-flavored version of Chapter 1.1's cooling-cliff anti-pattern: choosing a cheap, fast brownfield path for a roadmap that will cross into training-class density, then stranding capacity at a cost that would have funded a purpose-built liquid hall. → Chapter 5.4.