Rent vs Buy a GPU for AI (2026) — The Break-Even Is About 200 Hours a Month

Every cloud-GPU page on the internet answers this question, and every one of them sells GPU hours. We have the opposite bias: Compute Market is an affiliate marketplace for hardware you keep, and this page also carries a rental link we may earn on. So we get paid either way you decide — which is the most honest footing this comparison can stand on. Take both biases as declared and judge the arithmetic, which is simple enough to check on a napkin.

The Short Answer

Owning an AI GPU beats renting one at roughly 200 GPU-hours a month, sustained for three years — about 6–7 hours a day, every day. Below about 100 hours a month, renting is cheaper at every tier and every rate we checked. Past about 500 hours a month, owning is cheaper at every consumer tier and every rate we checked. In between, the answer depends entirely on which cloud tier you are honestly comparing against: SLA-backed on-demand capacity breaks even around 110–160 hours a month, while the cheapest marketplace and community rates push it past 200, and past 400 on the mid-range tiers.

The single variable that decides this is utilisation — how many hours a month the card is genuinely busy. Not how many hours you own it. Not how many hours your machine is switched on. Hours of actual inference or training. Most people who ask this question are, when they count honestly, somewhere between 20 and 60 hours a month, which is why the answer for most individuals is rent first, buy when the meter proves you wrong.

This post is the rental-side companion to build vs buy an AI workstation (which prices the machine two ways) and local AI electricity cost (which supplies the wall-power numbers used below). All three sit in the AI on a Budget hub.

The Formula

There is exactly one equation, and it has three inputs:

break-even hours = rig price ÷ (cloud $/hr − your electricity $/hr)

Renting costs the full hourly rate. Owning costs the hardware once, then only the power the card draws while it works. So every hour you run an owned card, you bank the difference between the cloud rate and your own electricity cost. Divide the hardware bill by that hourly saving and you get the number of busy hours at which the two paths cost the same. Divide that by however many months you plan to keep the machine, and you get a monthly threshold you can compare against your own habits.

Worked once, in full, so you can redo it with your own numbers: an RTX 5090 rig costs about $4,160 all-in and pulls roughly 725W at the wall under load. At the US average $0.16/kWh that is 0.725 × 0.16 = $0.116 an hour in electricity. Renting the same card on RunPod's Secure Cloud costs $1.19 an hour. The hourly saving from owning is $1.19 − $0.116 = $1.074. And $4,160 ÷ $1.074 = 3,873 hours. Over a 36-month ownership window, that is 108 hours a month.

The Break-Even Table

Buy-side prices are all-in working rigs, not bare cards — a GPU with no CPU, board, memory, PSU and SSD around it computes nothing. The totals come from the four tiers priced out in our build vs buy piece, at DDR5 spot pricing as of 31 August 2026. Wall-power figures come from our electricity cost guide. Cloud rates were read off each provider's own pricing surface on 19 October 2026 (sources at the bottom).

Three things fall out of that table that no cloud vendor's version of this post will tell you.

The faster the card, the easier it is to justify owning. The RTX 5090 has the lowest break-even of any consumer tier, despite being the most expensive. Cloud rates scale with how desirable a card is, so the hourly saving from owning a flagship is larger in absolute dollars than the saving on a mid-range card, and it outruns the extra purchase price. The intuition that "I should buy the cheap one and rent the expensive one" is backwards on cost grounds.

The used RTX 3090 is the hardest tier to justify buying. Not because the card is bad — it is the best value in VRAM per dollar, and our used 3090 head-to-head still recommends it. But the same second-hand glut that makes it cheap to buy makes it nearly free to rent: at a $0.22/hour marketplace median, a $2,000 build needs 12 hours a day for three years to pay itself off. If your workload fits in 24GB, the cloud has effectively commoditised that tier.

Nobody buying for a desk should buy an H100. Every row in that block needs 7–27 hours a day of genuine utilisation, for three years, before the purchase wins — and the most expensive node measured against the cheapest marketplace floor never wins at all, because 824 hours do not exist in a 730-hour month. The 80GB class is a rental. We cover why the specs still matter on the H100 PCIe page, but the finance answer is unambiguous.

One Verdict Per Usage Pattern

Five Things the Break-Even Hides

The table is the honest arithmetic of the two bills. It is not the whole decision. Here is what it leaves out, and which side of the ledger each one lands on.

1. Resale value (helps buying). A rented hour leaves you with nothing; a bought card can be sold. Halve the rig cost on resale and you halve the break-even hours — which is the single biggest thing the table omits, and it omits it because a resale price three years out is a forecast rather than a figure we can source.

2. Idle power (hurts buying). The break-even above charges owners only for active hours. A real desktop also sits there drawing current. At the 70–80W idle our electricity guide measures for a discrete-GPU desktop, leaving it on all month costs about $8–$9 before any inference happens — roughly $300 across three years. Apple Silicon's ~5W idle is why it keeps winning the always-on comparison.

3. Storage and egress (hurts renting). Cloud GPU rates are for the GPU. Model weights are not free: without a persistent volume you re-pull 40GB+ of a 70B model on every cold pod, and anything you move back out may be billed as egress. Budget for persistent storage on any provider you use seriously, and count it in the hourly rate when you redo this math.

4. Price volatility (cuts both ways). The purchase price is fixed the day you pay it. Marketplace rental rates are not: Vast.ai's medians move daily with supply and demand, and the figures here are a single 19 October 2026 snapshot. That is a risk if you are renting for three years and a reason to re-run the numbers before committing either way. In the other direction, a hardware shortage can lift the price of the card you were about to buy — see the DRAM shortage, which is currently doing exactly that to the memory line of every build sheet above.

5. Availability and friction (helps buying). Your own machine is never out of capacity, never queued, and never ten minutes from being usable. Cloud marketplaces are sometimes all three, especially for the one GPU everybody wants that week. This is not a cost, it is a tax on your attention, and it is the single most common reason people who did the math and chose renting end up buying anyway.

If You Decide to Rent, Here Is What the Tiers Actually Mean

The $0.34-to-$1.19 spread on the same RTX 4090 is not a pricing error. It is three different products:

• Marketplace / community capacity (Vast.ai, RunPod Community) is somebody else's hardware, priced by supply and demand, with reliability and network quality that vary by host. Cheapest per hour, least predictable. Right for batch work you can retry.
• SLA-backed on-demand (RunPod Secure Cloud, DigitalOcean GPU Droplets) is datacentre capacity with the uptime commitment you would expect from a cloud provider. Two to three times the marketplace rate. Right for anything serving a user.
• Reserved / committed is the same hardware at a discount for a term commitment — DigitalOcean's 12-month H100 rate is $3.26/hour against $4.41 on-demand. Right once your utilisation is high enough that you were about to buy anyway, which is the interesting case: a reserved contract is the option between renting and owning, and it moves the break-even in the cloud's favour.

Compare rates against the same card you were going to buy, not the cheapest card the provider lists. Most of the "cloud is 10x cheaper" claims online compare a spot-priced consumer card against the purchase price of a datacentre card.

How to Decide in Ten Minutes

1. Measure, do not estimate. Run your workload in the cloud for two weeks and read the invoice. Your billed hours are your utilisation, and they are almost always lower than the number you would have guessed.
2. Multiply by 36. Monthly busy hours × 36 = the hours you will actually use over a three-year ownership window.
3. Look it up in the table. Above the break-even row for your tier, buy. Below it, keep renting.
4. Check the VRAM ceiling before the price. If your model does not fit in the card you can afford, the break-even is irrelevant — use our VRAM requirements calculator first, then price the tier that fits.
5. Re-run it annually. Both sides of this equation move. Cloud rates fall as capacity builds out; hardware prices move with shortages.

If the answer comes out "buy", the GPU advisor picks the tier and best GPU for AI ranks the field. If it comes out "rent", you have saved several thousand dollars by doing one division, which is the best return on arithmetic available in this hobby.

Sources and Assumptions

• Cloud rates, read 19 October 2026. DigitalOcean GPU Droplets pricing (HGX H100 $4.41/GPU/hr on-demand, $3.26 on a 12-month reservation). RunPod pricing (Secure Cloud: RTX 5090 $1.19, RTX 4090 $0.89, H100 PCIe $2.89; Community Cloud: RTX 5090 $0.69, RTX 4090 $0.34). Vast.ai marketplace medians from its public hourly pricing feed, snapshot 15:30 UTC (RTX 5090 $0.71, RTX 4090 $0.49, RTX 3090 $0.22; H100 SXM floor $1.73). The feed declares its own terms in its license field — free for display and citation with attribution, which is what this is.
• Buy-side rig totals are the tracked build sheets from build vs buy an AI workstation, at DDR5 spot pricing of 31 August 2026. The RTX 4090 row is that post's Tier 3 sheet with the 4090's catalogue range swapped in for the 5090's. The H100 row adds a Supermicro GPU server barebones chassis to the card, because the card alone is not a machine.
• Wall power (725W / 520W / 420W / 700W under load) is from the wall-power table in local AI electricity cost 2026, which gives a source or a derivation for each one. Two of them are derivations rather than meter readings — the 4090's 520W and the 3090's 420W are vendor TDP plus system overhead, and that post flags the 3090 row as needing verification — so treat the two mid-range power columns as informed estimates. Electricity is $0.16/kWh, the US average per EIA, April 2026. Multiply the power column by your own rate over $0.16 to localise — at California or EU rates near $0.40/kWh, every break-even here gets meaningfully longer.
• Amortisation window is 36 months, chosen because it is the span over which an AI GPU stays competitive rather than any accounting convention. A 24-month window raises every hours/month figure by half; a 48-month window cuts it by a quarter.
• Excluded by choice: resale value, idle power, cloud storage and egress, your time, and any tax treatment. Sections above say which way each one pushes. We would rather give you an equation you can audit than a single number you have to trust.

The Verdict

Count your busy hours before you spend anything. Under 100 a month, rent — the hardware will not amortise and the cloud is genuinely the cheaper product. Over 500 a month, buy — you are paying rent on an asset you could own, and the premium compounds every month. In the 100–500 band, cost is close enough to a tie that it should not be the deciding factor, so decide on latency, privacy, and whether you want to think about provisioning at all.

The one number worth remembering: about 200 hours a month, for three years. Everything else is your own arithmetic, which is exactly how it should be.