Most guides tell you to pick the cheapest cloud GPU provider. That advice will cost you time and frustration. The cheapest per-hour rate means nothing if your instance disappears mid-generation or takes 20 minutes to cold-start. RunPod and Vast.ai serve different users, and picking the wrong one based on price alone is the most common mistake in cloud LLM inference.
Quick answer
Choose RunPod when uptime matters and Vast.ai when price does. RunPod charges roughly a 30-50% premium and buys you uptime guarantees, serverless endpoints and a polished developer experience. Vast.ai is a marketplace — cheaper, interruptible, and a good fit if you are comfortable with SSH and your workload can survive a restart. Plenty of people end up using both.
Try RunPod Cloud GPU→Who this is for
You have decided that cloud GPUs make sense for your LLM workflow — either because you run models occasionally, need burst access to high-end hardware, or want to avoid the upfront cost of buying a GPU. Now you need to pick between the two most popular providers.
Pricing comparison (2026)
| GPU | RunPod $/hr | Vast.ai $/hr | RunPod $/mo (8hr/day) | Vast.ai $/mo (8hr/day) |
|---|---|---|---|---|
| RTX 3090 | $0.22 | $0.12-0.18 | $53 | $29-43 |
| RTX 4090 | $0.34 | $0.20-0.30 | $82 | $48-72 |
| A100 40GB | $1.22 | $0.80-1.10 | $293 | $192-264 |
| A100 80GB | $1.19 | $1.00-1.40 | $286 | $240-336 |
| H100 80GB | $1.99 | $1.80-2.30 | $478 | $432-552 |
The gap is real on consumer cards and narrow-to-nonexistent on datacentre ones. Vast runs 18-45% under RunPod on a 3090 and 12-41% under it on a 4090, but on an A100 80GB or an H100 the ranges overlap — a bad night on Vast can cost more than RunPod does. Whatever the gap, it comes with trade-offs that matter.
Reliability and uptime
RunPod operates its own datacenter infrastructure alongside community hosts. Their “Secure Cloud” tier guarantees uptime SLAs, persistent storage, and consistent performance. Instances rarely disappear unexpectedly.
Vast.ai is a peer-to-peer marketplace. Individual hosts rent out their GPUs, which means pricing is competitive but availability fluctuates. Hosts can terminate instances with short notice, and hardware quality varies between providers.
For production workloads or anything where losing your session mid-inference is costly, RunPod’s reliability premium is worth paying.
Setup and ease of use
RunPod wins on developer experience. Their serverless endpoints let you deploy an LLM inference API without managing instances. Template-based deployment means you can have Ollama or vLLM running in under 5 minutes. The web terminal and monitoring tools work well.
Vast.ai requires more hands-on setup. You search the marketplace, filter by GPU type and price, and SSH into a bare machine. Docker templates help, but the experience is closer to renting a raw server than using a managed service.
Try Vast.ai Cloud GPU→Best for each use case
| Use Case | Winner | Why |
|---|---|---|
| Weekend experimentation | Vast.ai | Cheapest per-hour, no commitment |
| Production API | RunPod | Serverless endpoints, uptime SLAs |
| Fine-tuning jobs | Vast.ai | A100s at 30-40% less — see renting a GPU for fine-tuning |
| 70B model inference | RunPod | Consistent multi-GPU availability |
| Budget hobby use | Vast.ai | RTX 3090 at $0.12/hr is unbeatable |
| Team/business use | RunPod | Billing, teams, API management |
Which provider should you choose?
If you need reliable, always-available inference for a project or product, choose RunPod. The 30-50% price premium buys you uptime guarantees, serverless endpoints, and a polished developer experience. If you are experimenting, fine-tuning, or running batch jobs where occasional interruption is acceptable, choose Vast.ai. The savings are substantial — an RTX 4090 at $0.20/hr versus $0.39/hr adds up fast over hundreds of hours.
Common mistakes to avoid
- Choosing purely on $/hr. A $0.12/hr Vast.ai instance that gets terminated twice during a fine-tuning run costs more in wasted time than a $0.22/hr RunPod instance that runs to completion.
- Forgetting to stop cloud instances. Both providers charge while instances are running, even if idle. Set auto-stop timers on RunPod. On Vast.ai, manually destroy instances when done.
- Renting consumer GPUs for production inference. If you are serving an API, use datacenter GPUs (A100, H100) on RunPod’s Secure Cloud. Consumer GPUs on Vast.ai are for personal and experimental use.
- Not comparing against local GPU cost. Breaking even inside a year on a $2,200 RTX 4090 takes about $185/month of rental. At the $82/month an 8-hour day on RunPod costs, the card takes roughly 27 months to pay for itself — so the honest threshold is much higher than it looks. See our cloud vs local GPU comparison for the full math, or our cloud GPU TCO vs self-hosted analysis for detailed break-even curves by usage level.
Our recommendation
Start with RunPod if you value your time and need things to work consistently. Start with Vast.ai if you are price-sensitive, comfortable with SSH, and running workloads that can tolerate interruption. Many power users end up using both — RunPod for production inference and Vast.ai for experimentation and fine-tuning.
Try RunPod Cloud GPU→ Try Vast.ai Cloud GPU→ Check NVIDIA GeForce RTX 4090 on Amazon→Buy on Shopee SG→The best cloud GPU provider is the one that matches your tolerance for downtime, not the one with the lowest sticker price.
For a deeper look at when local GPUs beat cloud entirely, read our cloud vs local GPU guide. If you decide to go local instead, our best GPU for LLM server guide covers hardware for always-on inference.