Blog ▸ Is GPUaaS Cheaper Than AWS? The 2026 Numbers
GPU Infrastructure
Hyperscaler on-demand medians against committed GPUaaS.com bands, GPU by GPU, with the costs a sticker price leaves out and the workloads where each option actually wins.
Is GPUaaS Cheaper Than AWS? The 2026 Numbers
GPUaaS.com Team
GPU Infrastructure
October 20, 2026
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GPUaaS.com commits to roughly 30 percent lower cost than hyperscaler list pricing as a baseline, and on H100 and H200 this quarter the gap against published on-demand medians runs well past that: H100 at $2.50 to $3.00 against about $7.89, H200 at $2.75 to $3.50 against about $10.85, and B200 at $7.00 to $8.00 against about $14.24 per GPU-hour.
The honest answer to 'is GPUaaS cheaper than AWS' depends on which AWS price you are comparing against and what you are buying alongside the GPU. This post puts the October benchmark committed bands next to published hyperscaler on-demand medians for the three GPUs buyers ask about most, explains why the gap is often wider than the roughly 30 percent baseline, and names the costs that do not show up on either sticker price.
Key takeaways
H100 SXM: $2.50 to $3.00 committed on GPUaaS.com against a published on-demand median near $7.89 for comparable hyperscaler-class instances
H200: $2.75 to $3.50 committed against a published on-demand median near $10.85
B200: $7.00 to $8.00 committed against a published on-demand median near $14.24
The comparison is on-demand hyperscaler pricing against committed GPUaaS.com pricing; a hyperscaler's own committed or reserved pricing narrows the gap, though it still typically sits above the GPUaaS.com band
The GPUaaS.com rate includes no buyer fees and no cost to apply; a hyperscaler's effective cost often includes egress, storage and orchestration charges not visible in the per-GPU-hour rate
The deciding factor is usage pattern: workloads with a known, sustained GPU need for months at a time favor committed; short, bursty or unpredictable workloads favor on-demand regardless of the per-hour gap
◆ THE NUMBERS, GPU BY GPU
Committed GPUaaS.com bands against published hyperscaler on-demand medians
The comparison below is not apples to apples in every respect, so this says that plainly before the table rather than after it. The GPUaaS.com figures are committed-term bands from the October 2026 benchmark: a buyer signs a term, usually three to twelve months, and gets a dedicated node or cluster at that rate for every hour of the commitment. The hyperscaler figures are published on-demand medians: start and stop any time, no term, inside the provider's own platform.
Those are genuinely different products, and a fair comparison should say that up front rather than bury it in a footnote. What the comparison answers honestly is this: for a buyer who knows they need the GPU for a sustained period, which path costs less for that period. The answer, for H100, H200 and B200 this quarter, is the committed path by a wide margin.
◆ COMMITTED GPUaaS.com VS HYPERSCALER ON-DEMAND, PER GPU-HOUR, Q4 2026
GPU
GPUaaS.com committed
Hyperscaler on-demand median
Approx. gap
H100 SXM
$2.50 to $3.00
~$7.89
62% to 68% lower
H200 SXM
$2.75 to $3.50
~$10.85
68% to 75% lower
B200
$7.00 to $8.00
~$14.24
44% to 51% lower
GPUaaS.com bands from the October 2026 benchmark, committed terms. Hyperscaler medians are published on-demand rate-card figures for comparable instance classes, late September 2026, as cited in the benchmark. Different products: on-demand has no term, committed does. Contact for commitment terms. *Prices may vary depending on commitment terms.
◆ WHY THE GAP IS WIDER THAN A SIMPLE DISCOUNT
Term, not just margin, explains most of the difference
A roughly 30 percent framing undersells what is actually happening on H100 and H200 this quarter: the gap runs closer to two-thirds for both. Some of that is structural margin: a hyperscaler's on-demand price has to cover the cost of instant availability, no commitment, and a fully managed platform around the GPU. Some of it is the same effect documented across every GPU in the B200 pricing post: a buyer who names a term removes re-fill risk for the provider, and that risk reduction is worth real money.
Hyperscalers do offer their own committed and reserved pricing, and it is lower than their on-demand rate. A fair-minded version of this comparison should say plainly that a one-year or three-year hyperscaler reserved instance narrows the gap against GPUaaS.com's committed band. It typically does not close it, because a hyperscaler's reserved pricing still carries the overhead of the platform it sits inside, but the honest comparison is reserved-to-committed, not on-demand-to-committed, for a buyer who is already planning to commit on either side.
The B200 gap is narrower than H100 or H200 for a specific reason: B200 is newer, hyperscaler on-demand supply for it is tighter, and the committed GPUaaS.com band itself sits higher this quarter because B200 fleets were bought at higher unit cost, as the B200 pricing post covers. Even at the narrower 44 to 51 percent gap, committed still wins decisively for a buyer with a known term.
~68%
lower is the committed GPUaaS.com rate for H200 against the published hyperscaler on-demand median this quarter, the widest gap of the three GPUs compared here
GPUaaS.com, October 2026, hyperscaler medians as cited in the October benchmark
◆ WHAT THE STICKER PRICE LEAVES OUT
Costs that do not show up in the per-GPU-hour rate on either side
A per-GPU-hour comparison, however accurate, is not the full cost of either option. On the hyperscaler side, egress fees for moving data out of the platform, storage costs for checkpoints and datasets, and orchestration or managed-service charges layered on top of raw compute can add a meaningful percentage to the effective rate, and these rarely appear in the headline on-demand number a buyer first sees.
On the GPUaaS.com side, the committed rate is the rate: no buyer fees, and no cost to submit a request or get quoted. What a buyer gives up is the ability to stop paying mid-term if the workload ends early, which is the actual price of the discount rather than a hidden fee. A twelve-month commitment at $2.75 for H200 is a better deal than on-demand at $10.85 only if the workload actually runs for something close to twelve months; if it runs for six weeks, the comparison changes entirely.
Support and operational overhead cut both ways too. A hyperscaler's managed platform includes tooling, monitoring and an ecosystem of adjacent services that a committed GPUaaS.com cluster does not bundle by default, though the provider readiness checklist covers the support and SLA terms that come with a cluster contract. A buyer weighing the two options honestly should price in what they would otherwise build or buy to replace that tooling, not just the per-GPU-hour gap.
◆ WHEN ON-DEMAND IS ACTUALLY THE RIGHT CALL
The gap does not disappear, but the use case can make it irrelevant
None of this makes committed the right answer for every workload. A short-lived experiment, a burst of inference traffic that lasts days rather than months, or a proof-of-concept that might get cancelled next week are all cases where paying a premium for the ability to stop instantly is the economically correct choice, because the alternative is either an idle commitment or a scramble to find a buyer for unused months.
The practical dividing line is usage duration and certainty. A workload with a known, sustained GPU need for three months or more, where the team is confident it will run that long, should commit and capture the gap shown above. A workload with an uncertain runway, or genuinely spiky and unpredictable load, is better served on-demand until the pattern becomes clear enough to commit against. The committed vs on-demand post covers the same trade-off from the provider's side of the market.
For teams that want the cost advantage of committed pricing without locking in a full 8-GPU node before they are certain of the workload, packet.ai offers per-token inference access on the same hardware classes, which is often the better starting point before a cluster commitment makes sense.
See where this quarter's committed band actually lands.
Submit a spec and quotes come back within a few hours, sourced across 20+ vetted partners. No buyer fees. 8-GPU node minimum.
On committed terms, yes, by a wide margin this quarter: roughly 44 to 75 percent lower per GPU-hour depending on the GPU, compared against published hyperscaler on-demand medians. The comparison narrows against a hyperscaler's own reserved or committed pricing, which GPUaaS.com's committed band still typically beats.
$2.50 to $3.00 per GPU-hour on committed terms through GPUaaS.com this quarter, against a published on-demand median near $7.89 for comparable hyperscaler-class H100 instances, a gap of roughly 62 to 68 percent.
B200 is newer, hyperscaler on-demand supply is tighter for it, and the committed GPUaaS.com B200 band itself sits higher this quarter because early B200 fleets were bought at higher unit cost. H200 is a more mature, better-supplied part on both sides, which widens the gap to roughly 68 to 75 percent.
On the hyperscaler side, egress, storage and orchestration charges can add to the effective cost beyond the headline on-demand rate. On the GPUaaS.com side, the committed rate has no buyer fees, but the buyer commits to paying for the full term even if the workload ends early.
For short-lived, uncertain or genuinely bursty workloads where the ability to stop instantly is worth more than the per-hour savings. Committed pricing wins decisively for workloads with a known, sustained need running three months or longer.
Last reviewed: 21 October 2026. GPUaaS.com bands from the October 2026 benchmark. Hyperscaler on-demand medians are published rate-card figures for comparable instance classes, late September 2026, as cited in the benchmark. Different products; the comparison is directional. Bands move monthly. Get a committed quote on GPUaaS.com.
