GPU cloud costs have climbed steadily since 2022, and most developers don't really understand why. An NVIDIA H100 SXM on AWS (p4d.24xlarge equivalent) runs roughly $32–$36/hr on-demand. The same GPU on VOLT costs $2.99/hr. That amounts to a 10x gap. 

The difference comes down to three structural factors: 1) hyperscaler infrastructure overhead, 2) captive-market pricing power, and 3) the cost of idle capacity they built for peak demand. 

Understanding these pricing mechanics is paramount for AI startups and LLM researchers. Below, we get into exactly where your bill is going and what alternatives exist that are not only cheaper but also a competitive advantage for your project.

Where hyperscaler pricing originates

AWS, Azure, and GCP just charge you for much more than just GPU. They charge you for the entire stack that they’ve built around it: redundant power feeds, on-site hardware engineers, dedicated networking fabric (AWS's EFA, Google's Jupiter), multi-region replication, compliance certifications (SOC 2, HIPAA, FedRAMP), and enterprise sales teams. Every line item on that list gets amortized into the hourly rate. You’re paying for it, but you might not know it. 

The capital expenditure is significant and, in many cases, too much of a financial burden for startups and research teams. 

Google spent roughly $75B on infrastructure capex in 2024. Microsoft committed $80B. None of these hyperscalers are eating those costs. They’re passing them onto you, the customer, by financing them through your hourly rate. When you rent a p4de.24xlarge for an afternoon, you're partially paying down Google's or Amazon's debt service on a data center in Iowa, or whatever other rural cornfield they’re buying up in secret agreements.

Add to that the fact that hyperscalers built GPU capacity ahead of demand in 2022–2023. In that sense, idle capacity is never truly idle in their P&L, as it gets priced directly into utilization assumptions. If they planned for 70% utilization and actual utilization is 60%, the margin gap closes by increasing per-unit price. That is, by passing the cost onto your startup or research team. 

The Hyperscaler premium layers that hurt startups and researchers

On top of hardware cost, hyperscalers layer on:

Reserved instance pressure – On-demand rates are the "rack rate." The real price requires 1- or 3-year commitments. If your workload doesn't need sustained capacity, you're paying the no-commitment penalty every hour.

Managed service tax – Using SageMaker instead of raw EC2 GPU instances adds roughly 30–40% to the effective hourly rate for the same hardware.

Storage I/O – EBS volumes attached to GPU instances add $0.10–$0.16/GB-month, plus $0.065 per million I/O requests.

Egress fees – AWS charges $0.09/GB for data leaving the region. A 100GB model checkpoint transfer costs $9 in egress alone, on top of compute.

A real-world cost example

A typical LLM fine-tuning job on a single H100 SXM running for 72 hours:

VOLT H100 SXM (single GPU): $2.99/hr × 72 hrs = $215.28

AWS p4de.24xlarge (8× H100): $32.77/hr × 72 hrs = $2,359.44

If you need 4 GPUs instead of 8, AWS forces you to rent all 8. Remember, the instance is fixed; you have no choice. 

On VOLT, you provision exactly 4 × $2.99 = $11.96/hr, totaling $861.12 for 72 hours. The AWS equivalent for 4 GPUs is still $2,359 because the minimum unit is 8.

That $1,498 gap (per job) compounds fast across a team running multiple experiments per week.

Decentralized supply upends the hyperscaler math

VOLT aggregates idle GPU capacity from data centers, crypto miners, and independent operators who already own hardware and have fixed costs covered. Those suppliers don't need to recover hyperscaler-level overhead because there is $75B capex to amortize, or enterprise sales force, or egress fee monopoly.

The supply-side economics pass directly to the buyer. VOLT's marketplace currently lists thousands of GPUs across H100s, A100s, RTX 4090s, and L40Ss. Because supply is aggregated rather than centrally provisioned, utilization rates can be higher across the network. This has the positive effect of keeping prices competitive without requiring artificial scarcity pricing.

Granted, there are real tradeoffs. Decentralized nodes don't carry the same compliance certifications as AWS GovCloud. Multi-node InfiniBand fabric for 512-GPU runs isn't available the same way it is on a hyperscaler. So, if you need HIPAA-compliant compute or government certifications, centralized clouds remain the practical choice.

Why VOLT

VOLT is not trying to replace AWS for regulated enterprise workloads. The target is the 80% of GPU compute demand that doesn't need those certifications such as AI model training and fine-tuning, inference at scale, research experiments, and batch processing jobs where the priority is cost per GPU-hour, not compliance attestations.

The pricing model is transparent. H100 PCIe at $1.49/hr, H100 SXM at $2.99/hr, A100 80GB at $1.89/hr. All of this is posted publicly, and requires no sales calls, egress fees, or storage minimums. What you’re getting is spot-style pricing without the pressure to make a 2-year commitment.

For a team burning $15,000/month on AWS GPU instances for ML workloads, VOLT's pricing typically translates to $2,000–$4,000 for equivalent GPU-hours. The math here isn’t even close. 

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Related Questions

Why is AWS GPU compute more expensive than other cloud providers?

AWS charges for the full hyperscaler stack: redundant infrastructure, compliance certifications, egress bandwidth, managed service overhead, and enterprise support. This is all amortized into the hourly rate. Their GPU nodes also ship as fixed-size instances (8 GPUs minimum for some H100 configs), so you pay for hardware you don't use. The on-demand premium also covers the optionality of no commitment, which AWS prices at roughly 3× the equivalent reserved rate.

What is the cheapest GPU cloud for AI training?

Decentralized GPU marketplaces like VOLT consistently offer the lowest rates for H100 and A100 hardware: $1.49–$2.99/hr vs. $4–$36/hr on AWS, GCP, or Azure. The savings are largest for workloads that don't require regulated infrastructure. Lambda Labs and Vast.ai also offer competitive spot pricing in the $1.50–$2.50/hr range for A100s.

Do GPU cloud egress fees actually matter?

For large model checkpoints or frequent data transfers, yes. Moving 1TB of data out of AWS costs $92 in egress alone. Providers like VOLT don't charge egress fees, which matters most for iterative training workflows that regularly checkpoint large models or transfer datasets between cloud storage and compute nodes.

Why can't I just use spot instances to lower my AWS GPU bill?

Spot instances on AWS can cut costs 60–70%, but GPU spot capacity (particularly for H100 and A100) is frequently unavailable or interrupted mid-job. For training runs longer than a few hours, interruption risk often makes spot impractical without checkpointing infrastructure. Decentralized providers offer more predictable pricing without requiring reservation commitments.

How do hyperscaler GPU prices compare to buying hardware outright?

An H100 SXM costs roughly $25,000–$30,000 to purchase. At AWS on-demand rates ($32/hr), you've paid the hardware equivalent in about 800–900 compute hours, or roughly 33 days of continuous use. At VOLT's $2.99/hr, the same hardware cost is recovered after approximately 8,300 hours (345 days). For workloads under 33 days of annual usage, renting is cheaper than owning. But the platform you rent from determines whether that math works in your favor.