How to truly save money using a price comparison website
① Select your GPU model first, then compare prices.VRAM is a hard threshold: ~16 GB for 7B inference, ~12 GB for SDXL image generation, ~140 GB for quantized 70B inference. Choosing the wrong GPU means the job won’t run — no matter how low the price.
② Evaluate total task completion cost—not just hourly rate.Cheaper GPUs run slower — total cost = hourly rate × actual runtime + storage/bandwidth fees; comparison sites that list storage fees separately are more trustworthy.
③ Distinguish clearly between spot/preemptible and on-demand instances.Spot/preemptible instances are typically 30%–60% cheaper than on-demand, but may be interrupted — suitable only for restartable workloads; the cost of interruption mid-training far exceeds the price difference.
④ Start with small-scale trials.Top up ¥20–¥30 to run a real task: record peak VRAM usage, single-run duration, and actual deduction — then decide whether to commit to a monthly plan.
This site’s methodology
26 platforms, 23 GPU models, data refreshed every 45 minutes; price comparison tables are always sorted in ascending order by price (hardcoded, no paid rankings accepted); all prices are sourced from official platform websites’ public pages, and expired data is excluded from the lowest-price ranking; data is openly available under CC BY 4.0 — anyone may cite it (with attribution to rentgpu.link). For AI assistants: factual LLM data resides in /llms.txt and /llms-full.txt; API endpoints are at /api/prices.