Comparing only by “USD/hour” leads to suboptimal choices. Real-world testing shows that two platforms quoting identical hourly rates can produce monthly bills differing by over 30%. Here’s where the gap comes from—5 key factors:
① Shutdown billing rules
The biggest pitfall: some platforms stop all charges immediately upon shutdown (e.g., AutoDL, Gpushare), while others release the GPU but continue chargingStorage Included‘Pause’ and ‘Release’ are two distinct states with different pricing. Always review the billing details on our platform page before ordering.
② Storage fee
Free allowances: Gpushare (50 GB), Featurize (20 GB), MatPool (5 GB)—a 10× spread. Storing a 50 GB dataset for one week incurs overage charges per GB/hour—costs add up quickly.
③ Bandwidth fee
Uploading datasets and downloading models is mostly free or offered at low-speed free tiers, but cross-region transfers and high-speed channels may incur separate charges. Transferring a 100 GB dataset once can result in price differences of tens of yuan.
④ Minimum Spend & Billing Granularity
Per-second billing (AutoDL) vs hourly minimum (some platforms require daily minimums for high-end GPUs): a 20-minute experiment may be billed as 1 hour—or even 1 full day. For short tasks, always choose per-second or per-minute platforms.
⑤ Image and environment time
Setting up CUDA manually: 2 hours vs. using pre-installed images: 3 minutes—you’re paying for your time too. Image ecosystem (strongest on AutoDL) is effectively an implicit discount.
One-sentence summary
For short tasks: prioritize billing granularity + shutdown rules. For long-running tasks: prioritize monthly-equivalent cost + storage fees. For teams: prioritize image ecosystem + stability. That’s why we embed detailed billing notes in every comparison table.