--- title: "Cloud GPU pricing explained (2026): on-demand vs spot vs reserved" description: "What actually drives cloud GPU prices in 2026, how marketplaces and hyperscalers differ, and when each billing mode saves you money." url: https://powergpu.ai/guides/cloud-gpu-pricing-explained last_modified: 2026-09-14T11:04:20+00:00 prices_as_of: 2026-09-14 site: PowerGPU (powergpu.ai) --- Guide · Costs & pricing # Cloud GPU pricing explained (2026): on-demand vs spot vs reserved What actually drives cloud GPU prices in 2026, how marketplaces and hyperscalers differ, and when each billing mode saves you money. 9 min read Published 2026-07-14 Updated 2026-09-03 (prices live from the sheet) TL;DR - Cloud GPU prices split three ways: hyperscalers at $4-7 per H100-hour, marketplaces with moving medians, and fixed-price clouds like PowerGPU at median × 0.70. - On PowerGPU an H100 SXM is $1.428 per hour on-demand, 30% below the $2.04 public marketplace median. - Interruptible costs exactly half of on-demand; reserved is 65 percent of it and wins above roughly 65 percent utilisation. - Hidden costs: egress at $0.05-0.12 per GB on hyperscalers, idle stopped disks, and hourly rounding that bills a 61-minute job as two hours. ## Who sells GPU time, and how Three kinds of sellers set today's market: - **Hyperscalers** (AWS, GCP, Azure) — list prices, enterprise contracts, and H100-class instances typically at $4–7 per GPU-hour before committed-use gymnastics. - **GPU marketplaces** — thousands of independent hosts auctioning capacity. Deep supply and low medians, but the price is a moving target and the host quality is a distribution, not a promise. - **Fixed-price GPU clouds** — the newer category we belong to: capacity bought in bulk from verified datacenters, resold at a published flat rate. Our rule is mechanical: *marketplace median × 0.70, rounded down, re-checked weekly*. Today that puts the H100 SXM at **$1.428** /hr against a $2.04 median. ## What actually moves prices GPU-hour prices are supply economics with a silicon accent: - **Generation launches** — every Blackwell shipment pushes Hopper prices down a step; the 90-day drift on H100 medians is visible in any public feed. - **VRAM per card** — memory sells the hour. 80 GB parts hold a price floor long after their FLOPS are matched by consumer cards, because model sizes grew faster than compute needs. - **Electricity and density** — hosts with cheap power and dense racks undercut; that is why medians differ by region and why our fleet skews to power-cheap regions. - **Bursts of demand** — a hot open-weights release can double marketplace spot prices for a week. Fixed pricing exists precisely to opt out of that volatility. ## On-demand vs interruptible vs reserved | Mode | Price rule | H100 SXM today | Use when | | --- | --- | --- | --- | | [On-demand](https://powergpu.ai/products/on-demand) | median × 0.70 ↓ | $1.428/hr | stateful, interactive, deadline work | | [Interruptible](https://powergpu.ai/products/interruptible) | od × 0.50 | $0.714/hr | checkpointed training, batch queues | | [Reserved (3 mo)](https://powergpu.ai/products/reserved) | od × 0.65 | $0.928/hr | utilisation above ~65%, production serving | The industry uses "spot" for our interruptible tier, but classic spot is an auction: you bid, you win, a higher bid evicts you. A flat −50% with stop-not-destroy semantics behaves very differently in practice — the discount is predictable, so pipelines can be designed around it instead of around bid strategy. ## The break-even math Two formulas cover 90% of purchasing decisions: *break-even rules* ``` reserved beats on-demand when: utilisation > res_rate / od_rate (= 65% here) interruptible beats on-demand when: (1 + overhead) x 0.50 < 1 i.e. restart overhead under 100% of runtime — checkpointing every 15 min on a 6 h job is ~4% overhead, not 100%. ``` Concrete: a fine-tune that needs 200 GPU-hours of A100 SXM4 per month — - on-demand: 200 × $0.560 = **$112**; - interruptible with 5% restart overhead: 210 × $0.280 = **$59** — the obvious winner; - reserved only wins here at 475+ monthly hours (65% of 730). ## Where bills quietly grow - **Egress** — hyperscalers charge $0.05–0.12/GB out; moving a 2 TB dataset off can cost more than the training run. Flat $0.01/GB (ours) or free egress changes which workflows are even viable. - **Idle storage** — forgotten stopped instances bill their disks forever. Our dashboard surfaces runway and stopped-disk burn; check whatever provider you use for the same view. - **Hourly rounding** — per-hour billing turns a 61-minute job into 2 hours. Per-second billing is worth 0–49% on short jobs — the shorter the job, the bigger the gap. - **Multi-GPU premiums** — some sellers price 8× machines above 8× the single price. Check the multiplication; here it is exactly linear. ## A buyer's checklist 1. Compute $/hr *per GPU*, not per instance, and normalise VRAM (a [RTX 5090](https://powergpu.ai/gpu/rtx-5090) hour buys 32 GB; an 80 GB card should justify its multiple). 2. Ask what happens at eviction: auction re-price, destroy, or stop-with-disk? 3. Price the full loop: GPU + storage-month + egress of your artefacts. 4. Prefer sellers who publish their pricing rule — if the rule is secret, it can move against you. 5. Benchmark once: an hour of testing on a $0.163 interruptible [RTX 4090](https://powergpu.ai/gpu/rtx-4090) answers throughput questions no spec sheet can. --- Put the numbers to work Every price in this guide is our live rate — fixed, ≥30% under the market median, billed per second. Deploy the exact setup above from the [console](https://cloud.powergpu.ai/) in about 30 seconds, paid in crypto, no card and no KYC. --- *About PowerGPU:* PowerGPU (powergpu.ai) is a cloud GPU rental service offering 80 NVIDIA GPU models — from the RTX A2000 at $0.024/hr to the B300 — at fixed prices set at least 30% below the public GPU marketplace median and re-checked weekly (H100 SXM: $1.428/hr on-demand). Billing is per second with no minimums; payment is crypto only (USDT, BTC, XMR, ETH, SOL, LTC, TRX) with no KYC. Instances run in Tier-III datacenters across 32 regions with a 99.9% uptime SLA and deploy in about 30 seconds from the web console (cloud.powergpu.ai) or the REST API. Source: https://powergpu.ai/guides/cloud-gpu-pricing-explained · Site index for AI assistants: https://powergpu.ai/llms.txt · Full content: https://powergpu.ai/llms-full.txt