Identical card. Identical performance. A five-fold spread in what you pay. Here is the 2026 map of every way to get AI compute, what each one actually costs, and how to tell which one you should be on.
There is a number in AI infrastructure that most teams never see, because it is buried across a cloud invoice, an API bill, and a spreadsheet nobody owns. It is the effective hourly cost of a single H100, and in 2026 it ranges from roughly eighty cents to four dollars and twenty cents depending entirely on how you got access to that card. Same silicon. Same 80GB of HBM3. Same performance. A five-fold spread.
That gap is the single largest controllable cost in most AI budgets, and the default choice, on-demand cloud, sits at the expensive end of it. What follows is the honest map: the four ways to run AI compute in 2026, what each genuinely costs, where the break-even points fall, and the constraint that decides all of it, which turns out to be electricity rather than chips. We have also just opened two new services on the back of it, and I will be upfront about where they fit.
The four paths, and what they actually cost
Every team running AI compute is on one of four paths, whether or not they chose deliberately. The differences are not about quality of hardware. They are about who owns the card, who owns the building, and who carries the risk of it sitting idle.
On-demand cloud is the default and the most expensive. Hyperscaler H100 instances run roughly three to four dollars per GPU-hour, with Azure toward the top of the range near seven. You get capacity in minutes with no commitment, which is genuinely valuable when speed is the only thing that matters. You also pay a premium for that availability every hour, including the hours your GPUs sit idle, and egress fees land on top.
Specialist GPU clouds are the same cards at a fraction of the price. Providers focused purely on GPU capacity rent H100s in the range of roughly $1.49 to $3.00 per hour, commonly 50 to 75 percent cheaper than hyperscalers for identical hardware. The trade is usually less enterprise tooling and support. For a great many workloads that trade is obviously worth making, and a lot of teams are simply overpaying out of habit.
GPU hosting, renting dedicated capacity on a committed term, drops the effective rate again. You are no longer paying the premium for instant elasticity you do not use, and you get consistent, dedicated hardware rather than whatever the scheduler hands you. This is the sweet spot for a workload that has stabilized but is not yet large enough to justify buying.
Buying and colocating is the cheapest per hour and the most demanding. You purchase the hardware, roughly $25,000 to $30,000 for an H100 PCIe and $35,000 to $40,000 for SXM5, then place it in a facility that supplies power, cooling, network, and hands. Colocation commonly runs 40 to 60 percent cheaper in total cost of ownership than hyperscaler cloud for stable workloads, with break-even against renting typically landing somewhere between six and eighteen months of sustained use depending on utilization and what you paid.
The pattern is consistent across every serious analysis: the more predictable your workload, the more you are overpaying by renting it on demand. And the less predictable it is, the more that flexibility is genuinely worth paying for.
The break-even, stated plainly
Here is the rule of thumb, with the honest caveats attached.
If your GPUs would run at high utilization for more than about a year to eighteen months, owning and colocating almost always wins. If your usage is spiky, experimental, or you are still validating whether a model works at all, renting wins and it is not close, because owned hardware sitting idle is the most expensive compute there is. The failure mode that costs teams the most is not picking the wrong path, it is staying on the launch path forever: prototyping on on-demand cloud, which is correct, then serving production traffic on it two years later, which is not.
Utilization is the whole ballgame. A card at 30 percent utilization on a committed term can easily cost more per useful hour than on-demand at a higher headline rate. Before moving, measure what your fleet actually does across a normal month, not what you hope it will do. The numbers above are current mid-2026 market ranges and they move; treat them as a framework, not a quote.
The real constraint is power, not chips
Now the part that explains why this market looks the way it does, and why the cheap end of that five-fold spread exists at all.
The binding constraint on AI compute in 2026 is not the availability of GPUs. It is power and the ability to cool it. A standard enterprise rack was built for about 5kW. A dense H100 cluster wants at least 20kW per cabinet. A 2026-era Blackwell rack can demand 100 to 140kW, and liquid cooling stops being an optimization and becomes a requirement. Most conventional data centers simply cannot deliver that per-rack density, which is why "qualified but no power" is the most common reason a deployment stalls.
So the question becomes: who already operates buildings with megawatts of cheap power, industrial-scale cooling, and staff who keep thousands of hot chips alive around the clock? Bitcoin miners. That is the entire job description, and they have been doing it under brutal margin pressure for a decade. We wrote about how mining and AI became the same business, and this is what that convergence looks like in practice: the power contracts, substations, and cooling built for hashing turn out to be exactly what an AI rack needs. The people who solved cheap power years ago are now the people who can offer AI compute at the bottom of that price range.
What we have just launched, and who it is for
That is the background to two new services, and I would rather explain the fit than pitch them.
GPU hosting is for teams that want dedicated GPU capacity without buying hardware or running a facility. You get committed access on industrial-rate power with cooling and staff handled, which is the middle path above: cheaper than on-demand cloud because you are not paying for elasticity you do not use, and simpler than ownership because the facility is somebody else's problem. It suits a workload that has settled into a predictable shape but does not yet justify capital expenditure.
AI data center colocation is for teams that already own hardware, or are ready to buy it, and need somewhere that can actually power and cool it. This is the cheapest path per hour for sustained workloads, and the one where the power question decides everything. If you have a DGX system or a dense liquid-cooled cluster and have been told "no capacity" by conventional facilities, this is the gap it fills.
If you want the whole site rather than a rack in it, turnkey mining farms and AI data centers covers acquiring a built facility outright, which is the converged asset at the center of all of this: one building that can mine Bitcoin, host AI, or both.
Choose the hardware before you choose the home
One thing worth saying because it saves real money: the newest chip is frequently not the right chip, and the most common expensive mistake is buying for the spec sheet rather than the workload.
Before committing to any path, work out what you actually need to run. The GPU and AI benchmark scores real inference performance and, more usefully, shows what fits in what memory, which is the constraint that decides whether a model runs at all. If your workload fits in 32GB, a consumer card can outperform a data-center part on value by a wide margin, and we broke that down in detail in the RTX 5090 versus H100 comparison. If you need more, the H100, H200 and B200 compared covers where each one earns its price. When you know what you need, the AI hardware catalog is where to source it.
Get this order right. Pick the hardware for the job, then pick where it lives. Doing it the other way around is how teams end up with expensive cards that do not fit their model and a contract that does not fit their utilization.
The same logic, on the Bitcoin side
If you came to this from mining rather than AI, the economics are identical because the physics are identical, and the same decision tree applies.
Owning a miner and running it on residential power is the equivalent of on-demand cloud: convenient, and usually the most expensive option per unit of output. ASIC hosting is the equivalent of GPU hosting, your machine on industrial-rate power with cooling and staff, which is what makes modern hardware viable when a home outlet does not. Regulated US farms add compliance and institutional-grade operations on top. Hydro mining is the efficiency frontier, running the newest sub-10 joules-per-terahash machines on water cooling, and it is the closest mining analogue to the liquid-cooled AI racks above. Cloud mining is renting hashrate outright, with no hardware at all.
Wherever the machines sit, mining pools determine how you actually get paid, and because we build our own hardware, how we build ASIC miners explains what goes into a machine and why efficiency claims deserve scrutiny. On the power question specifically, green and sustainable mining covers where the electricity actually comes from, which matters increasingly for both mining and AI as buyers ask harder questions about energy sourcing.
Model it before you commit
The single best thing you can do before signing anything is put your own numbers into a model, because every recommendation above inverts depending on your power rate and your utilization.
For mining, the ASIC profitability calculator and the BTC mining calculator let you test a machine against your actual electricity cost rather than a marketing figure. If you would rather see it than model it, the free 24-hour miner test lets you watch real hardware mine before you spend anything, which is the most honest form of a demo we know how to offer.
For AI, the equivalent discipline is measuring utilization and memory fit first, then pricing the four paths against that. Both sides come down to the same two questions: what does your power cost, and how hard will the hardware actually run.
The bottom line
The same H100 can cost you eighty cents or four dollars and twenty cents an hour, and the difference is not the card, it is the arrangement. On-demand cloud buys speed and flexibility at a premium. Specialist clouds cut that substantially for the same silicon. Hosting removes the elasticity premium for workloads that have settled. Colocation is cheapest per hour once utilization is high and sustained, provided you can find a facility that can actually power and cool modern density, which is the real bottleneck in 2026.
None of these paths is universally correct, and anyone claiming a guaranteed saving without seeing your utilization is guessing. But most teams are further up that price curve than they need to be, out of inertia rather than analysis. Measure your utilization, check what your model actually needs, and price the alternatives honestly. The five-fold spread is not a market inefficiency you have to accept. It is a choice, and it is usually being made by default.
The practical takeaway
Measure your utilization across a normal month before you renew anything. If it is high and steady, you are almost certainly overpaying on demand. If it is spiky, keep renting and ignore anyone telling you to buy.
● Dedicated GPU capacity without buying hardware: GPU hosting
● Somewhere that can actually power and cool your rack: AI data center colocation
Originally published on MillionMiner. Pricing is a mid-2026 snapshot and moves weekly; savings depend on your utilization and power rate.
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