# Monthly vs hourly GPU pricing: how to find your break-even > Monthly vs hourly GPU pricing: the break-even formula, a utilization table with our prices, the extras hourly bills add and when hourly is the better deal. Source: https://overgpu.com/compare/monthly-vs-hourly-gpu-pricing Updated: 2026-10-23 Publisher: OverGPU (https://overgpu.com/) **In short:** Divide the monthly price by the hourly rate you would pay: if your GPU will work more hours than that, monthly billing wins; if fewer, pay by the hour. ## Overview **Monthly vs hourly GPU pricing** comes down to one number: how many hours your GPU will actually work. Hourly billing, on-demand or spot, charges for the time an instance runs. Monthly billing, which is how OverGPU rents dedicated GPUs, charges one flat price whether the GPU is busy or idle. For GPU rental, monthly vs hourly is not a matter of taste: the cheaper option flips at a break-even point you can compute in a minute. This page shows how, with our real prices and the costs that hourly bills tend to add. ## Monthly vs hourly GPU pricing: the short answer The more hours a GPU works each month, the more a flat monthly price pays off; the fewer hours, the more hourly billing makes sense. Where the crossover sits depends on the hourly rate you would actually pay, so compute it rather than guess. Steady workloads such as production inference, long training runs or a team's shared development server tend to land on the monthly side. Short experiments and occasional bursts land on the hourly side. There is also a third model: reserved terms. In 2026 many providers prefer 1-year contracts for GPU capacity, which means a commitment twelve times longer than a monthly plan. A monthly term keeps the commitment to one month while still giving you a fixed price. ## The break-even formula The break-even is the number of hours at which both options cost the same: > **Break-even hours per month = monthly price ÷ hourly rate you would pay.** Use more hours than that and monthly costs less; use fewer and hourly costs less. Divide by 730, the average number of hours in a month, to get the break-even utilization. Two worked examples with an H100 at $2,190 a month, using illustrative hourly rates rather than quotes from any provider: - At $5.00 an hour, the break-even is 2,190 ÷ 5.00 = 438 hours, or 60% of the month, about 14.4 hours a day. Above that, monthly costs less. At 200 hours, hourly costs $1,000, less than half the monthly price. - At $2.50 an hour, the break-even is 876 hours, more than the month contains. Hourly costs less even at full use, unless the extras below change the picture. ## What you pay per hour actually used The same math, read the other way: divide the monthly price by the hours you actually use. The table shows the effective cost per GPU-hour used for four of our single-GPU servers. Each cell is also a break-even rate: if the hourly rate you can get, extras included, is below the number in your row, hourly costs less at that utilization; if it is above, monthly does. | Utilization (hours of 730) | L40S, $890/mo | H100, $2,190/mo | H200, $3,090/mo | B200, $4,590/mo | | --- | --- | --- | --- | --- | | 100% (730 h) | $1.22 | $3.00 | $4.23 | $6.29 | | 75% (547.5 h) | $1.63 | $4.00 | $5.64 | $8.38 | | 50% (365 h) | $2.44 | $6.00 | $8.47 | $12.58 | | 25% (182.5 h) | $4.88 | $12.00 | $16.93 | $25.15 | At full use, a monthly server costs its per-GPU-hour equivalent; at 25% it costs four times that per useful hour. On 8-GPU servers the full-use figure per GPU is a little lower, for example $2.89 on the HGX H100 and $4.09 on the HGX H200. All configurations are on our [pricing page](https://overgpu.com/pricing). The table also shows why idle hours behave differently under each model. Hourly billing rewards switching off. With a flat price, an idle hour costs nothing extra, so filling it with batch work such as evaluations, embedding backfills, synthetic data generation or a nightly fine-tune lowers your effective cost per useful hour. ## Costs an hourly bill often adds The hourly rate is rarely the whole bill. Before comparing, add the items that apply to you: - **Egress.** Hyperscalers typically bill outbound data separately, per GB, which adds up when you serve users or move checkpoints and datasets out. - **Storage while stopped.** Stopping an instance usually stops the GPU charge, but persistent disks and volumes keep billing, and checkpoints and datasets are rarely small. - **Setup time.** Each fresh instance means pulling containers, downloading weights and warming caches. Paid hours spent on setup are hours not spent on work. - **Re-provisioning.** In 2026 GPU capacity is tight and on-demand stock is often sold out. Releasing a GPU to save money can mean not getting one back when you need it. - **Preemption.** Spot or interruptible capacity is discounted but can be reclaimed at short notice. Lost progress, restarts from checkpoints and idle waiting all cost money. - **Quotas and price changes.** Hyperscalers typically require quota approval for GPU instances, and hourly rates move: OverGPU's October 2026 survey of public price lists found the median on-demand price per GPU-hour up roughly 13% for H100, 27% for H200 and 20% for B200 over the past year. ## When hourly billing is the better deal Monthly billing is not always the right answer, and you should pick the model that fits the work. Hourly wins when: - You need GPUs for a short burst: a weekend experiment, a one-off batch job or a demo. - Your utilization is well below the break-even, for example a GPU used a few hours a week. - You want to compare several GPU types for a few hours each before choosing one. - Your demand is spiky: dozens of GPUs for a day, none for the rest of the month. You can also combine both: a monthly server for the steady baseline, such as production inference or a team's development box, and hourly capacity for occasional bursts. Our minimum term is one month, so for anything shorter, hourly is the honest recommendation. Our article on [dedicated vs shared GPUs](https://overgpu.com/blog/dedicated-vs-shared-gpus) covers the other half of the trade-off. ## What a monthly server at OverGPU includes Our prices are flat, in US dollars and billed monthly in advance from a prepaid balance that you top up in crypto, and your price is fixed for each paid month: - **Whole GPUs:** Assigned to you alone, with no slicing or time-sharing, and root access over SSH. - **No setup fee:** No egress fees within the included bandwidth: 20 TB a month on single-GPU servers and 50 TB on 8-GPU servers, fair use, inbound not counted. - **One-month minimum:** With automatic renewal from your balance, an email reminder before each renewal, and cancellation from your dashboard any month with no fee; the server runs until the end of the paid month. - **99.9% monthly availability SLA:** With service credits of 10% to 100% of the monthly fee. - **Delivered in under 10 minutes:** After payment. If you know you need capacity for 6 to 12 months or several servers, ask for term pricing in a ticket from your dashboard once you have an active server, giving the duration and quantity. Details are in [how billing works](https://overgpu.com/help/how-billing-works) and [bandwidth and egress](https://overgpu.com/help/bandwidth-and-egress). ## How to run the numbers for your workload 1. Estimate the hours your GPU will do useful work this month, including nights and weekends if it serves users. 2. Find the hourly rate you would actually pay, and add egress, storage and setup time from the list above. 3. Divide the monthly price by that rate. If your hours exceed the result, go monthly; if not, go hourly. The [GPU cost calculator](https://overgpu.com/tools/gpu-cost-calculator) runs this comparison for every GPU we offer, and our [service level agreement](https://overgpu.com/legal/sla) sets out what the monthly price commits us to. ## Frequently asked questions ### How do I calculate the break-even between monthly and hourly GPU pricing? Divide the monthly price by the hourly rate you would pay, including extras such as egress and storage. The result is the number of hours per month above which the monthly price costs less; a month averages about 730 hours. ### Does OverGPU offer hourly billing? No. We rent dedicated GPUs on monthly terms only, billed in advance with a one-month minimum. If you need a GPU for a few hours or days, an hourly provider is the better fit for that job. ### What utilization makes a monthly GPU worth it? It depends on the hourly rate you compare against. For an H100 at $2,190 a month, the cost per hour actually used is $4.00 at 75% utilization and $6.00 at 50%, so monthly wins at your utilization whenever the hourly rate you would pay, extras included, is higher than that figure. ### Can I cancel a monthly GPU server at any time? Yes. You can cancel any month with no fee, and the server keeps running until the end of the paid month; the current month is not refunded. ### Are there egress fees on monthly servers? Not within the included bandwidth: 20 TB a month on single-GPU servers and 50 TB on 8-GPU servers, under fair use. Inbound traffic is not counted. ## Related pages - [GPU cost calculator: monthly vs hourly GPU rental](https://overgpu.com/tools/gpu-cost-calculator): Monthly totals, break-even hours and the extra costs to count before you decide. - [GPU server pricing: every monthly GPU rental price](https://overgpu.com/pricing): From $449 a month: flat monthly GPU rental with bandwidth, IPv4 and 24/7 support included, no setup or egress fees. See every price. - [How GPU server billing works](https://overgpu.com/help/how-billing-works): A flat monthly USD price, paid in advance from a prepaid balance you top up in crypto. No setup or egress fees. - [H100 rental cost in 2026: what you actually pay](https://overgpu.com/blog/h100-rental-cost-2026): SXM vs PCIe, monthly vs hourly billing, egress and storage, plus a break-even formula to compare any quote before you sign. - [Dedicated vs shared GPU: what changes when you share](https://overgpu.com/blog/dedicated-vs-shared-gpus): How vGPU, MIG, time-slicing and shared hosts affect memory, consistency, security and debugging, and when sharing is fine. - [GPU servers for LLM inference, running 24/7 at a flat price](https://overgpu.com/solutions/inference): KV cache sizing, FP8 and FP4, and 20–50 TB of bandwidth included. See the sizing math.