Cheapest GPU Cloud Providers 2026

If your priority is squeezing maximum compute out of every dollar, four GPU clouds dominate the budget tier in 2026: RunPod (best value), Vast.ai, TensorDock, and Hyperstack. Hyperscalers (AWS, GCP, Azure) are systematically 3–5× more expensive for raw GPU compute and only make sense if you need their proprietary ML services.

The cheapest GPU clouds use one or more of these tactics:

• Marketplace model (Vast.ai) — peer-to-peer auction drives prices to commodity
• Community / interruptible tier (RunPod, Vast) — host can take instances offline, 50-70% cheaper
• Consumer GPUs (RTX 3090/4090 instead of datacenter A100/H100) for compatible workloads
• EU sovereign infrastructure (OVH from €0.36/h) — lower energy and real-estate costs than US datacenters

Reality check: the cheapest tier requires fault-tolerant code (checkpointing, retry logic). For always-on production inference, add 50–80% to the sticker price for "Secure" or "On-Demand" tiers.

Spiky inference? The idle hours are the real cost. A $0.16/h instance left running around the clock is $3.84/day whether or not requests arrive. If your endpoint is bursty rather than constant, a serverless GPU that bills per second and scales to zero drops the idle hours entirely: you pay for active compute, not wall-clock time. The trade-off is a cold-start penalty on the first request after scale-down. Serverless stops paying off once utilization is high enough (very roughly a third of the day) that a dedicated cheap instance is simply cheaper to leave on.

Why no AWS, Azure or GCP here: all three do have sub-$1 GPU instances — AWS g4dn.xlarge and Azure NC4as_T4_v3 are both $0.526/h, GCP's g2-standard-4 is $0.71/h. But the first two are 2018-era T4 cards, and a T4 at $0.53 buys less throughput than a RunPod RTX A5000 at $0.16. Match the GPU class instead and the hyperscalers land 3–5× higher: an 8× H100 node is $55.04/h on AWS p5 and $88.49/h on GCP a3-highgpu-8g, against $1.99/h per H100 on RunPod Community.

What interruptible actually costs you

The sticker price is not the thing that bites. Take a job needing 20 GPU-hours of real compute, checkpointed every 30 minutes, on an interruptible instance that gets reclaimed roughly every 3 hours. Each preemption throws away up to a half-hour of work and costs another 10–15 minutes to land a new node and reload weights.

That works out to about 7 preemptions, 1.7 hours of recomputed work, and 17% more wall-clock. Interruptible still wins on money, and usually will. It loses on everything else, and you only get that price if you already wrote checkpoint-and-resume logic that works. The $3 you save is real, but it is not worth an engineering afternoon unless you are running that job repeatedly or at much larger scale.

The break-even is roughly this: use the cheap tier for anything batch, repeated, or restartable. Pay for the secure tier when a failed run costs you more than the price gap, which is nearly always true for a demo the next morning or an inference endpoint with users on it.