Let's get the headline out of the way: AWS is the most expensive place to rent a GPU in 2025, and it's not even close. A single H100 on AWS costs $8.46/hr. The same H100 on Cudo Compute costs $1.87/hr. That's a 4.5x markup for the same silicon. We pulled real pricing data from 17+ providers to show you exactly how much you're overpaying — and when it might actually be justified.
The Price Comparison: H100 On-Demand Across 12 Providers
All prices below are for a single H100 GPU, on-demand (not spot), as of February 2025. Data sourced from our real-time comparison tool, which refreshes every 6 hours.
| Provider | H100 $/hr | vs AWS |
|---|---|---|
| Vast.ai | $1.30 | 6.5x cheaper |
| Cudo Compute | $1.87 | 4.5x cheaper |
| Hyperstack | $2.08 | 4.1x cheaper |
| Verda | $2.29 | 3.7x cheaper |
| RunPod | $2.70 | 3.1x cheaper |
| Lambda Labs | $2.89 | 2.9x cheaper |
| Nebius | $2.95 | 2.9x cheaper |
| OVHcloud | $3.19 | 2.7x cheaper |
| Crusoe | $3.90 | 2.2x cheaper |
| Scaleway | $4.08 | 2.1x cheaper |
| Latitude.sh | $7.97 | 1.1x cheaper |
| AWS | $8.46 | — |
Read that table again. AWS is 2.9x more expensive than Lambda Labs and 4.5x more expensive than Cudo Compute. Vast.ai comes in at an absurd 6.5x cheaper than AWS. Even Latitude.sh — which is not cheap by any means — undercuts AWS. Every single provider in our database is cheaper than AWS for H100 on-demand.
The Monthly Bill: Where It Gets Painful
One H100 running 24/7 for a month (720 hours):
- AWS: $6,091/month
- Lambda Labs: $2,081/month
- Cudo Compute: $1,346/month
- Vast.ai: $936/month
Switching from AWS to Lambda Labs saves you $4,010/month per GPU — that's $48,120/year. For a team running 4 GPUs, that's nearly $200,000 in annual savings. Enough to hire another engineer. And if you move to Cudo or Vast.ai, the savings are even more dramatic.
Perspective check: If you're a startup spending $24,000/month on 4x H100s on AWS, you could run the same workload on Cudo Compute for $5,384/month. That's $18,616/month you could put into compute, salaries, or runway. Use our comparison tool to find the current cheapest rates.
So Why Does Anyone Still Use AWS?
This is the fair question, and the answer isn't "people are dumb." AWS has real advantages that smaller providers can't match:
- Existing infrastructure. If your data lives in S3, your databases are on RDS, and your networking is configured in a VPC, moving GPU workloads to another provider means dealing with data transfer latency, egress costs, and VPN tunnels. That's real engineering friction.
- Enterprise contracts. Large companies often have pre-negotiated EDP (Enterprise Discount Program) rates that bring AWS GPU prices down 20–40%. At scale, that narrows the gap — though it still doesn't close it.
- Compliance. If you need SOC2, HIPAA, FedRAMP, or PCI-DSS compliance, AWS has years of audit reports. Smaller GPU clouds are often still working through certification.
- SLAs. AWS offers contractual uptime guarantees. Most GPU-focused clouds offer best-effort, which is fine for research but potentially unacceptable for production inference endpoints.
When AWS Is Unjustifiable
The compliance and infrastructure arguments only apply if you actually need them. For a huge number of GPU workloads, AWS is simply unjustifiable:
- Research and experimentation. You're training models, running ablations, testing architectures. You don't need SLAs. You need cheap GPUs.
- Startups without AWS lock-in. If you're starting from scratch, there's no reason to begin with the most expensive provider. Start cheap, migrate later only if compliance demands it.
- Any workload where data is portable. If your dataset is under 1 TB, you can move it to any provider in under an hour. The egress cost is negligible compared to months of GPU savings.
- Fine-tuning. A fine-tuning run takes hours to days. Spin up an instance on Vast.ai or Lambda Labs, upload your data, run the job, download the weights, terminate the instance. You don't need a persistent cloud ecosystem for this.
The Bottom Line
AWS GPU pricing is a legacy tax. You're paying for the brand, the ecosystem, and the compliance certifications — not for better hardware. The H100 on AWS is physically identical to the H100 on Lambda or Vast.ai. If you need AWS's ecosystem, pay the premium with eyes open. If you don't, you're lighting money on fire. Check the pricing trends — the gap between AWS and the rest has been widening, not narrowing. The market is speaking. Listen to it.
