Every GPU cloud provider claims to be the cheapest. Most of them are lying — or at least being very creative with the truth. When Vast.ai advertises GPUs starting at $0.01/hr, they're technically correct: you can rent a V100 spot instance for a penny an hour. What they don't tell you is that it's a consumer-grade card sitting in someone's basement, the connection might drop at any moment, and there's no SLA, no support, and no guarantee your data isn't being logged. "Cheapest" and "best value" are very different things.
We track pricing across 18 GPU cloud providers and over 5,025 GPU instances daily. Here's what every provider actually charges at the low end, and which ones are worth your money depending on what you're building.
The Real Cheapest Prices, Provider by Provider
This table shows the absolute cheapest GPU instance available from each provider, regardless of GPU model or instance type. We update these numbers daily.
| Provider | Cheapest Price | GPU | Type |
|---|---|---|---|
| Vast.ai | $0.01/hr | V100 | Spot |
| Verda | $0.05/hr | V100 | Spot |
| Vultr | $0.06/hr | A16 | On-Demand |
| Azure | $0.07/hr | T4 | Spot |
| RunPod | $0.07/hr | RTX 3070 | Spot |
| AWS | $0.11/hr | T4 | Spot |
| GCP | $0.11/hr | T4 | Spot |
| Hyperstack | $0.15/hr | RTX A4000 | On-Demand |
| Cudo Compute | $0.24/hr | V100 | On-Demand |
| CloudRift | $0.39/hr | RTX 4090 | On-Demand |
The Three Tiers of GPU Cloud
Not all GPU clouds are created equal. The market has naturally stratified into three tiers, and understanding which tier a provider falls into will save you from nasty surprises.
Tier 1: Marketplace Providers (Cheapest, But Risky)
Vast.ai and Verda operate peer-to-peer GPU marketplaces. Individual hosts — ranging from small data centers to people with gaming rigs in their garages — list their hardware, and you rent it. Prices are rock bottom because there's no enterprise infrastructure behind it.
The tradeoff is real. Hardware quality varies wildly. Uptime is not guaranteed. You might get a machine with faulty RAM, a flaky network connection, or a host who decides to pull the plug mid-training run. For experimentation, prototyping, and batch jobs that can tolerate interruptions, marketplace providers are unbeatable on price. For anything production-facing, steer clear.
Tier 2: Mid-Tier Providers (Best Balance)
This is where the smart money goes. Providers like Vultr, RunPod, Lambda, Hyperstack, and CloudRift operate their own data centers (or have exclusive agreements with colocation partners). You get real infrastructure — dedicated networking, proper cooling, NVMe storage — at prices that are 40–70% cheaper than AWS or GCP.
Vultr at $0.06/hr for an A16 on-demand is a steal. RunPod at $0.07/hr for spot RTX 3070 instances is excellent for small inference workloads. Lambda has carved out a niche as the go-to for serious ML training with competitive H100 pricing and solid multi-GPU setups. These providers are where most startups and independent researchers should start.
Tier 3: Enterprise Hyperscalers (Expensive, But Reliable)
AWS, GCP, and Azure charge a significant premium for their GPU instances — but you're paying for the entire ecosystem. IAM, VPCs, managed Kubernetes, auto-scaling, compliance certifications, 24/7 enterprise support, and SLAs that actually mean something. If you're a regulated enterprise, if you need SOC 2 compliance, or if your infrastructure team is already deep in the AWS ecosystem, the convenience tax might be worth it.
That said, even within the hyperscalers, there are deals to be found. Azure spot T4 instances at $0.07/hr are competitive with mid-tier providers. AWS and GCP both offer T4 spot at $0.11/hr. The trick is using spot aggressively and avoiding on-demand pricing wherever possible.
Our recommendation: For production inference, start with Vultr, RunPod, or Lambda. For training, look at Lambda, Hyperstack, or Nebius. For experimentation and prototyping, Vast.ai spot instances can't be beat on price.
What "Cheapest" Actually Means for Your Workload
The cheapest GPU isn't the one with the lowest sticker price — it's the one that minimizes your total cost for a given workload. A $0.01/hr V100 is useless if your model needs 48GB of VRAM. A $1.87/hr H100 is a waste if you're serving a 7B parameter model to 10 requests per minute.
Think about what you actually need: How much VRAM does your model require? Do you need guaranteed uptime or can you handle interruptions? Are you training for days or running inference for seconds? The answers to these questions matter more than the per-hour price on any provider's landing page.
Use our GPU price comparison tool to filter by GPU model, VRAM, provider, and instance type. It's the fastest way to find the actual cheapest option for your specific use case, updated daily with live pricing from the providers we track.
The Hidden Costs Nobody Talks About
Per-hour GPU pricing is only part of the story. You also need to consider egress fees (AWS charges $0.09/GB for data leaving their network), storage costs (NVMe vs network-attached makes a huge difference for data-heavy training), and setup time. If it takes you two hours to configure networking and drivers on a cheap provider, you've already blown your savings over a more expensive provider with pre-configured ML images.
RunPod and Lambda both offer pre-built PyTorch and TensorFlow images that boot in minutes. Vast.ai often requires you to bring your own Docker image and debug driver compatibility. That friction is invisible in the pricing table but very visible in your productivity.
Bottom line: the cheapest provider depends on who you are and what you're doing. Don't chase the lowest number in a table — chase the lowest total cost for your workload. Check our trends page to see how prices have been shifting, and lock in your choice before the next wave of price changes.
