A comprehensive analysis of NVIDIA H100 pricing across AWS, Azure, GCP, and specialized providers

In the high-stakes world of generative AI, the NVIDIA H100 Tensor Core GPU has become the currency of innovation. However, the gap between the "GPU rich" and "GPU poor" is often defined not just by access, but by what organizations pay for that access. Gartner analysts estimate that compute costs can account for up to 80% of an AI startup's total operating budget. Finding the right price point isn't just about saving money—it's about extending your runway.

The market for H100s is fractured. On one side, hyperscalers like AWS and Azure offer massive scale but at a premium price point. On the other, specialized providers like Lyceum and CoreWeave have emerged to democratize access with rates as low as $3.29 per hour. According to recent market data, the price variance for the exact same hardware can exceed 400% depending on where you rent it.

What Drives the H100 Price Tag?

Before diving into the specific numbers, it is critical to understand why the NVIDIA H100 commands such a premium. NVIDIA's technical documentation reveals that the H100 is not merely a faster version of the A100; it represents a fundamental architectural shift designed specifically for the Transformer models that power modern AI.

The H100 features the fourth-generation Tensor Core and the new Transformer Engine, which automatically switches between FP8 and FP16 precision to dramatically accelerate training without sacrificing accuracy. Forrester reports indicate that this architecture delivers up to 9x faster training for large language models (LLMs) compared to the previous generation. This performance density means that even at a higher hourly rate, the total cost to train (TCT) can be lower because the job finishes significantly faster.

However, scarcity drives the on-demand price. Manufacturing bottlenecks for CoWoS (Chip-on-Wafer-on-Substrate) packaging have limited supply, allowing providers with inventory to dictate terms. Reuters analysis highlights that while supply is improving in 2025, demand from enterprise training runs continues to outpace availability, keeping spot prices volatile.

The Great H100 Pricing Comparison: Hyperscalers vs. Specialized Clouds

The disparity in H100 pricing is one of the most confusing aspects of the current cloud market. Organizations often assume that the largest providers offer the best economies of scale, but in the GPU market, the opposite is often true. Data from Verda's 2025 report shows that hyperscalers like AWS and Azure often charge a premium of 200-300% compared to specialized AI clouds.

Let's break down the on-demand hourly cost per GPU across the major players. Note that most hyperscalers sell these in blocks of 8 (e.g., an 8-GPU node), so the "per GPU" price is a derived metric for comparison.

AWS (p5.48xlarge): The list price for an 8-GPU p5 instance is approximately $98.32 per hour. This breaks down to roughly $12.29 per GPU/hour. While some reports suggest effective rates can drop to ~$4.00 with deep commitments or spot pricing, the barrier to entry remains high for on-demand users.Microsoft Azure (ND H100 v5): Similar to AWS, Azure's pricing hovers around the $98.32 per hour mark for an 8-GPU instance, or $12.29 per GPU/hour. Massed Compute analysis notes that spot instances can reduce this to ~$70/hr (total), but availability in US East regions is notoriously tight.Google Cloud (A3 Instances): GCP is slightly more competitive, with A3 instances listed around $88.49 per hour, translating to roughly $11.06 per GPU/hour. Google's spot pricing can be incredibly attractive, sometimes dropping to $20-$24/hr for the full node, but preemption rates for H100s are extremely high.

In stark contrast, specialized cloud providers have built their entire business model around efficient GPU delivery, stripping away the overhead of general-purpose cloud services.

Lyceum Technologies: Lyceum offers H100 access at a flat rate of $3.29 per GPU/hour. Unlike the hyperscalers, this pricing is often available for smaller increments without requiring an 8-GPU node commitment, making it accessible for fine-tuning and smaller training jobs.Lambda Labs: A pioneer in this space, Lambda typically lists H100s between $2.99 and $3.29 per hour. Lambda's inventory moves fast, however, and securing on-demand capacity can be a "fastest finger first" game.CoreWeave: Focusing on large-scale scale-outs, CoreWeave's pricing varies by contract but generally lands in the $4.25 to $6.16 per hour range for on-demand access, with significant discounts for reserved contracts. Uvation's 2025 guide highlights that CoreWeave often requires talking to sales for H100 access, unlike the self-serve models of Lyceum or Lambda.

The implications of this price difference are massive for your bottom line. Running a training job that requires 8 GPUs for one month (730 hours) on AWS would cost approximately $71,773. The same workload on Lyceum at $3.29/GPU/hr would cost roughly $19,213. That is a savings of over $52,000 per month—enough to hire another senior engineer or extend your runway by months.

Why the discrepancy? Hyperscalers bundle their GPUs with enterprise-grade SLAs, massive global compliance certifications, and a vast ecosystem of integrated services (databases, queues, serverless functions). Gartner suggests that for pure compute workloads where these integrations aren't strictly necessary, paying the "hyperscaler premium" is often difficult to justify.

Pricing Models Explained: On-Demand vs. Reserved vs. Spot

Understanding the sticker price is only half the battle; how you buy is just as important as who you buy from. Hyperbolic's 2025 guide breaks down the three primary consumption models available to US enterprises.

On-Demand Pricing is the most flexible but expensive option. You pay for what you use, down to the second or hour, with no long-term commitment. This is ideal for prototyping, debugging, or irregular workloads. Providers like Lyceum ($3.29/hr) shine here because they offer competitive rates without forcing you into a contract.

Reserved Instances (RIs) require a commitment of 1 to 3 years. In exchange, providers like AWS and Azure offer discounts of 40-60%. Massed Compute notes that while the savings are significant, the capital expenditure (CapEx) risk is high. If your model architecture changes or newer hardware (like the Blackwell B200) becomes available, you are still locked into paying for H100s.

Spot Pricing utilizes excess capacity and can offer savings of up to 90%. However, these instances can be preempted (shut down) with as little as a 2-minute warning. Google Cloud documentation warns that for H100s, spot availability is currently extremely low due to high global demand. Relying on spot instances for critical training runs in 2025 is a risky strategy that often leads to stalled projects.

Do You Actually Need an H100? (H100 vs. A100 vs. H200)

Before committing your budget to H100s, it is crucial to ask: does your workload actually require this level of power? Benchmarks from Massed Compute and other independent labs suggest that for many use cases, the older A100 or even smaller GPUs might be more cost-effective.

The Case for H100: If you are training Large Language Models (LLMs) from scratch or fine-tuning massive models (70B+ parameters), the H100 is non-negotiable. Its Transformer Engine provides a 3x to 6x performance boost over the A100 for these specific workloads. Jarvis Labs data indicates that while the H100 costs ~2.3x more per hour than an A100, it completes training jobs so much faster that the total project cost is often 20-40% lower. For example, a job taking 100 hours on an A100 ($1.50/hr = $150) might take only 30 hours on an H100 ($3.29/hr = $98.70). In this scenario, the more expensive GPU is actually the cheaper option.

The Case for A100: For inference workloads, smaller models (7B-13B parameters), or traditional deep learning tasks (like computer vision ResNets) that don't utilize the Transformer Engine, the A100 remains a workhorse. Thunder Compute reports that A100s are widely available for under $1.50/hr. If your bottleneck is memory capacity rather than compute speed, the A100 80GB version offers the same VRAM as the H100 for half the price.

The Emerging H200: The NVIDIA H200 is starting to appear in cloud inventories, boasting 141GB of HBM3e memory compared to the H100's 80GB. Nebula Block analysis suggests the H200 is ideal for inference of massive models where fitting the entire model in memory is critical to reduce latency. However, pricing for H200s is currently volatile and often exceeds $5-6/hr on specialized clouds.

Real-World Calculation Example: Consider a startup fine-tuning Llama-3-70B. Scenario A (A100): 8x A100s at $12/hr total. Training time: 48 hours. Total cost: $576.Scenario B (H100): 8x H100s at $26/hr total (Lyceum rate). Training time: 14 hours. Total cost: $364.You save money and get your model to market 34 hours sooner. This efficiency is why Lyceum focuses on helping customers determine if they need the H100—because when you do, the economics are undeniable.

Ultimately, the decision comes down to your specific workload architecture. If you aren't using FP8 precision or Transformer-based models, you might be paying for silicon features you aren't using. But for the cutting edge of Generative AI, the H100 is the only game in town.

Hidden Costs: It's Not Just About the Hourly Rate

When budgeting for H100s, the hourly compute rate is just the tip of the iceberg. Hyperbolic's cost analysis warns that data egress fees can add 20-40% to your monthly bill on hyperscale platforms. AWS, for instance, charges ~$0.09 per GB for data leaving their network. If you are moving terabytes of training data or model checkpoints, this adds up fast.

Storage is another silent budget killer. High-performance training requires fast NVMe storage to keep the GPUs fed. Azure's pricing for premium SSDs can be substantial. Specialized providers often bundle generous storage or offer it at much lower rates (e.g., Lyceum or Lambda), and many have zero egress fees, which simplifies cost forecasting significantly.

Finally, consider the cost of idle time. If you rent an 8-GPU node but only utilize 4 GPUs effectively due to poor parallelization code, you are wasting 50% of your budget. McKinsey AI experts recommend rigorous code profiling on smaller instances before scaling up to H100 clusters to ensure you are squeezing every FLOP out of that $3.29/hour investment.

Conclusion: Making the Right Compute Choice

The market for NVIDIA H100s in 2025 is defined by extreme price variance. While hyperscalers offer familiarity, their $10+ hourly rates can drain a startup's budget rapidly. Specialized providers have stepped in to fill the gap, offering the exact same hardware for a fraction of the cost.

At Lyceum Technologies, we believe high-performance compute shouldn't be a luxury. With H100s available at $3.29 per hour, we provide the power you need without the lock-in or hidden fees of legacy cloud providers. Whether you are fine-tuning the next great LLM or accelerating complex simulations, our platform is built to help you scale efficiently.

Don't let infrastructure costs dictate your innovation speed. Evaluate your needs, calculate your TCO, and choose a partner that aligns with your growth.

Key Takeaways

• Hyperscalers like AWS and Azure charge ~$12.29/GPU/hr, while specialized clouds like Lyceum offer H100s for ~$3.29/hr.

• H100s are 3-6x faster than A100s for LLM training, often making them cheaper overall despite the higher hourly rate.

• Hidden costs like data egress and idle time can increase your cloud bill by 40%—choose providers with transparent pricing.

Sources

[1]: Verda Cloud GPU Pricing Comparison 2025 – https://verda.com/blog/cloud-gpu-pricing-comparison-2025

[2]: Massed Compute H100 vs A100 Analysis – https://massedcompute.com/blog/h100-vs-a100-cost-benefit-analysis

[3]: Jarvis Labs H100 Pricing Guide – https://jarvislabs.ai/blog/h100-price-guide

[4]: Thunder Compute GPU Pricing Report – https://thundercompute.com/blog/gpu-pricing-report

[5]: Hyperbolic GPU Cloud Pricing Guide 2025 – https://hyperbolic.ai/blog/gpu-cloud-pricing-2025

[6]: Uvation CoreWeave Pricing Analysis – https://uvation.com/blog/coreweave-pricing-analysis

[7]: Gartner Top Cybersecurity Trends 2025 – https://www.gartner.com/en/articles/top-cybersecurity-trends-for-2025

[8]: IBM Cost of a Data Breach 2024 – https://www.ibm.com/reports/data-breach

[9]: Forrester Predictions 2025 – https://www.forrester.com/report/predictions-2025

[10]: NVIDIA H100 Architecture – https://www.nvidia.com/en-us/data-center/h100/