“Cloud GPUs are always cheaper than buying hardware.” I hear this constantly, and it is wrong for most people. But there are real scenarios where renting makes perfect sense — and if you are renting, RunPod and Vast.ai are the two platforms worth considering.
Quick answer: RunPod is better for reliability and ease of use. Vast.ai is cheaper but less predictable. I use RunPod for production workloads and Vast.ai for experimental batch jobs.
Try RunPod Cloud GPU→ Try Vast.ai Cloud GPU→Who this is for
You need GPU power beyond what your local hardware provides. Maybe you are training a model that needs multiple A100s. Maybe you need a one-time burst of compute for fine-tuning. Or maybe you simply do not want to invest $2,000 in a GPU you will use sporadically.
Platform comparison
| Feature | RunPod | Vast.ai |
|---|---|---|
| A100 80GB price | ~$1.89/hr | ~$1.20/hr |
| H100 price | ~$3.49/hr | ~$2.50/hr |
| RTX 4090 price | ~$0.69/hr | ~$0.40/hr |
| Interface | Clean, modern | Functional, dense |
| Serverless | Yes | No |
| Docker support | Full | Full |
| Spot instances | Yes | Yes (community) |
| Uptime | 99.5%+ (secure cloud) | Varies by host |
| Billing | Per-second | Per-second |
| Storage | Network volumes | Varies |
Prices fluctuate. Vast.ai is a marketplace — individual host prices change constantly.
Try RunPod GPU Instances→Which platform should you choose?
- Need reliability? RunPod. Their secure cloud instances run in professional datacenters with guaranteed uptime. I have had Vast.ai machines disappear mid-training. That does not happen on RunPod secure cloud.
- Watching every dollar? Vast.ai. Prices are 30-40% lower on average. Community instances are dirt cheap. You accept more risk for the savings.
- Running serverless inference? RunPod only. Their serverless platform lets you deploy models as API endpoints with auto-scaling. Vast.ai has nothing comparable.
- Short burst training? Either works. For a 2-hour fine-tuning job, both platforms get the job done. Save money on Vast.ai, save hassle on RunPod.
Common mistakes to avoid
- Not using spot/community instances for fault-tolerant jobs — if your training can checkpoint and resume, use cheaper interruptible instances. The savings are significant.
- Leaving instances running overnight — I have accidentally left a $3.49/hr H100 running for 14 hours. Set billing alerts on both platforms.
- Ignoring data transfer costs — uploading a 50GB dataset takes time and sometimes money. Use network volumes on RunPod or persistent storage on Vast.ai.
- Defaulting to cloud when local makes more sense — if you use GPUs more than 4-5 hours daily, buying an RTX 4090 or RTX 5090 pays for itself within months.
Final verdict
| Scenario | Best Choice | Why |
|---|---|---|
| Production inference | RunPod | Serverless + reliability |
| Budget training | Vast.ai | 30-40% cheaper |
| One-off fine-tuning | Either | Both work well |
| Multi-GPU training | RunPod | Better orchestration |
If your AI workloads are consistent enough to justify hardware, check the best GPU for AI guide. For workstation setups that can double as cloud alternatives, see the best workstation GPU for AI breakdown.
Buy RTX 4090 Instead of Renting→Cloud GPUs make sense for burst compute and experimentation. But if you are running local inference every day, the math almost always favors buying a card outright.