Hyperscaler vs Specialized GPU Cloud: A Total Cost Comparison

On a total cost basis, a specialized GPU cloud usually costs less than a hyperscaler for GPU-heavy training and inference, because the GPU-hour gap is around 2× and the extra line items mostly add to the hyperscaler side. Hyperscalers still win when your data, managed services, or committed spend already live there, so the answer depends on what the rest of the bill looks like.

• The GPU-hour gap is about 2×: AWS lists H200 Capacity Blocks (p5e) at $6.866 per GPU-hour and H100 (p5) at $5.970, while GMI Cloud lists H200 from $3.20 and H100 from $2.60 per GPU-hour (all as of October 2026).

• The GPU line is not the whole bill: AWS charges $0.09 per GB for the first 10 TB of monthly internet egress after a 100 GB free allowance, and its Business Support+ plan adds 9% of the first $10,000 of monthly charges and 7% of the next $70,000.

• Identical GPUs do not deliver identical throughput: NVIDIA routinely sees 8 to 12% training gaps between cloud deployments and its reference architecture on the same hardware, including a GB200 NVL72 case that ran 12 to 14% slower in a virtual machine than on bare metal.

• A worked month makes the gap concrete: One 8× H200 node with 20 TB of egress and Business Support+ comes to about $44,964 on AWS Capacity Block pricing, against an $18,688 GPU line on GMI Cloud plus whatever storage and networking the quote adds (October 2026 prices).

• Hyperscalers still win some cases: When terabytes of training data, a managed data stack, or a large committed-spend agreement already sit with one hyperscaler, moving GPUs elsewhere can add egress and engineering costs that erase the rate gap. The comparison only works when every line is on the table.

GPU prices stopped falling in 2026. Cast AI’s 2026 State of Kubernetes Optimization Report, as reported by SDxCentral, noted that AWS raised H200 Capacity Block prices by 15% in January 2026, and AWS’s own Capacity Blocks pricing page now says reservation rates are updated with supply and demand, with the next update scheduled for January 2027. When the biggest line on an AI budget moves like that, the choice of where to run GPUs deserves a real total cost comparison rather than a glance at two hourly rates.

The problem is that hourly rates are the only numbers both sides publish clearly. A hyperscaler’s GPU instance arrives bundled with CPUs, memory, local disk, and a network fabric, and then meters egress, storage, inter-zone traffic, and support on top. A specialized GPU cloud usually publishes a lower per-GPU rate and quotes the rest. Comparing the two means pricing the same workload, line by line, and then adjusting for how much useful work each GPU-hour actually delivers.

This guide builds that comparison. It defines what total cost should include, compares current list prices for H100, H200, and B200, walks through the line items beyond the GPU, explains why identical hardware can deliver different throughput, and prices one 8× H200 node for a month on both models. It closes with when each type of cloud is the better choice.

What Total Cost Means for GPU Workloads

Total cost of ownership for cloud GPUs is everything you pay to get a given amount of training or inference done, not the price of the accelerator alone. For a monthly budget, it helps to write it down explicitly.

monthly total cost = GPU compute + storage + data transfer + networking + support + engineering time

Two adjustments turn that monthly figure into something you can compare across providers. The first is utilization: GPU-hours you reserve but do not use still count, and Cast AI’s 2026 report measured average GPU utilization of just 5% across the Kubernetes clusters it analysed. The second is throughput: If one environment finishes the same training step faster, each of its GPU-hours is worth more.

effective cost per unit of work = monthly total cost ÷ (useful work completed in the month)

Useful work can be training steps, tokens served, or images generated. The point is to compare providers on dollars per result, because that is what the business actually buys.

The GPU-Hour: Comparing Like with Like

Hyperscaler List Prices

AWS sells its NVIDIA GPU capacity mainly as 8-GPU instances. On-demand, the p5.48xlarge with eight H100 GPUs lists at $55.04 per hour in US East (N. Virginia), or $6.88 per GPU-hour, according to instance trackers that mirror AWS’s price list. AWS also sells EC2 Capacity Blocks for ML, which reserve GPU instances for a fixed future window and are charged up front. In October 2026, AWS lists Capacity Blocks at $5.970 per H100-hour on p5, $6.866 per H200-hour on p5e, $7.895 per H200-hour on p5en (which adds faster networking), and $14.208 per B200-hour on p6-b200, with the Linux operating system at no extra charge.

Google Cloud’s a3-highgpu-8g, also eight H100 GPUs, is listed at about $88.49 per hour on demand in us-central1 by third-party trackers that mirror Google’s price sheet, or roughly $11.06 per GPU-hour. Committed-use and savings plans lower both hyperscalers’ effective rates, but the discounts depend on term, region, and negotiation.

Specialized GPU Cloud Rates

Specialized GPU clouds usually price per GPU-hour directly. As of October 2026, the GMI Cloud pricing page lists H100 from $2.60 per GPU-hour, H200 from $3.20, B200 from $5.00 with limited availability, and GB200 from $8.00. The page describes the resources as dedicated NVIDIA GPUs with no shared environments, available on demand or reserved, with lower unit costs through commitment-based pricing. Committed rates are quoted per customer.

What the Node Includes

A per-GPU rate only compares cleanly when the rest of the node is comparable. AWS’s p5.48xlarge pairs eight H100 GPUs with 192 vCPUs and 2,048 GB of memory, and Google’s a3-highgpu-8g with 208 vCPUs and 1,872 GB. When you request a quote from any provider, ask for the full node specification per GPU: CPU cores, system memory, local NVMe capacity, and the GPU-to-GPU network (InfiniBand or Ethernet, and how many NICs per node). A cheaper GPU-hour attached to a starved CPU or a slow fabric is not cheaper per result.

The Line Items Beyond the GPU

Data Transfer

Egress is the line item most often missing from GPU comparisons. AWS does not charge for data coming in, but internet egress is tiered: The first 100 GB per month is free, then $0.09 per GB up to 10 TB, with lower rates at higher volumes. An inference service that returns 20 TB of responses, embeddings, or generated media a month pays about $1,741 in egress alone at those rates. Cross-region replication and traffic between availability zones are metered separately.

Data transfer also decides where data should live. If training datasets stay in a hyperscaler’s object storage while GPUs run elsewhere, every epoch that streams data across clouds pays egress. Teams that split workloads usually copy datasets once to storage next to the GPUs, then pay egress only on results.

Storage and Checkpoints

Model weights, datasets, and checkpoints need fast storage near the GPUs, and every provider prices it separately from compute or bundles a fixed amount of local NVMe with each node. For training, checkpoints are the line that grows quietly: A full training state with optimizer data takes many times the size of the model weights, and teams often keep dozens of them. Price storage on both sides at the capacity you will actually keep, including snapshots.

Support

Production GPU workloads need fast answers when a node fails. On AWS, paid support is a percentage of the bill: Business Support+ charges 9% of the first $10,000 of monthly AWS charges, 7% from $10,000 to $80,000, and lower percentages above that, with Enterprise plans carrying higher minimums. Because the fee scales with spend, an expensive GPU line also raises the support line. Specialized GPU clouds typically include engineering support in the service or set it in the contract, so ask what response times and escalation paths come with the quote.

Commitments and Access to Capacity

Both models discount for commitment, but in different ways. AWS Capacity Blocks are prepaid for a fixed window at the rate in force when you buy, and the rate itself moves with supply and demand. Savings Plans and reserved terms lower on-demand prices in exchange for a one- or three-year commitment. GMI Cloud’s pricing page describes commitment-based savings for reserved capacity and sustained deployments, with the option to start on demand and move to committed terms as workloads stabilize. Whichever you choose, compare committed offers at the same term and GPU count.

Throughput per Dollar: Why Identical GPUs Perform Differently

Two clouds can rent you the same GPU model and still deliver different results per hour. In its Exemplar Cloud lessons, NVIDIA reports that partner clusters on identical H100, GB200 NVL72, and GB300 NVL72 systems routinely trail its reference architecture by 8 to 12% on the same workload and batch size, usually because of several small configuration issues stacking up. Its case studies include a GB200 NVL72 FP8 pre-training job that ran 12 to 14% slower in a virtual machine than on bare metal, an H100 cluster that lost 12% to CPU power states and NUMA misbinding before tuning cut the gap to 3%, and a B200 deployment that ran 13 to 53% below reference because an NCCL topology file never reached the container.

That is why throughput belongs in the cost comparison. The Exemplar Cloud program requires a deployment to reach 95% of reference throughput, and GMI Cloud has been recognized as an NVIDIA Exemplar Cloud on GB300 NVL72 systems for training. Whatever provider you evaluate, run your own model on a short trial and measure step time or tokens per second before you sign a long commitment.

effective price per GPU-hour = list price ÷ (relative throughput × average utilization)

A provider that is 10% cheaper per hour but 10% slower per step saves nothing. A provider that is half the price and within a few percent on throughput saves close to half.

Worked Example: One 8× H200 Node for a Month

To make the comparison concrete, take one 8× H200 node running all month (730 hours) for an inference service that sends 20 TB of results to the internet. The AWS column uses the p5e Capacity Block rate, Linux, internet egress at list rates, and Business Support+. The GMI Cloud column uses the H200 on-demand starting rate. Storage is left out of both columns because it depends on the workload, and GMI’s storage, networking, and support terms come from the quote rather than a public price list.

On these assumptions, the hyperscaler side costs about 2.4 times the GMI GPU line. The gap leaves plenty of room for the items still to be quoted: Even if GMI’s storage, egress, and support added several thousand dollars, the total would remain well below the AWS figure. A throughput penalty does not close it either. If the specialized node delivered 10% less work per hour, its compute would cost the equivalent of about $20,764 for the AWS node’s output, still less than half.

Two things can change the picture. A large committed-spend agreement can lower the hyperscaler rate well below list, so put your negotiated rate in the AWS column if you have one. And if the 20 TB of data originates in the hyperscaler and the results must flow back, the egress line appears twice. Rerun the table with your own volumes before deciding.

When a Hyperscaler Is the Better Choice

A hyperscaler is often the right home for GPUs when the GPU work is a small part of a large workload that already lives there. If your data lake, feature store, and serving stack run on one cloud and the GPU bill is modest, the cost of moving data out and maintaining two environments can outweigh the rate difference. The same is true when a company has a committed-spend agreement it needs to consume, credits it would otherwise lose, or compliance approvals that cover only that provider.

Hyperscalers also make sense for bursty experimentation where spot capacity is acceptable and jobs can tolerate interruption, and for teams that depend on tightly integrated managed services more than on raw GPU throughput. In those cases, use the total cost model above to check the premium you pay, then decide whether the integration is worth it.

When a Specialized GPU Cloud Wins

A specialized GPU cloud usually wins when GPUs dominate the bill. Sustained training runs, steady inference traffic, and fine-tuning programs that keep nodes busy for weeks all multiply the hourly gap, and at around 2× on list prices that gap is hard to recover elsewhere. Specialized providers also tend to offer bare metal servers and dedicated, single-tenant GPUs, which avoid the virtualization overheads NVIDIA describes and give engineers full control of drivers, NCCL settings, and topology files.

A hybrid pattern is common and often the cheapest overall: Keep the application, data platform, and customer-facing services on the hyperscaler, and run training and heavy inference on a specialized GPU cloud. The total cost model tells you whether that works for your volumes, because the egress between the two is the line that decides it.

Running GPU Workloads on GMI Cloud

GMI Cloud is an NVIDIA-partner AI cloud with three ways to consume GPUs. Serverless inference scales to zero with no idle cost and suits variable traffic. Dedicated endpoints give a model its own GPUs once traffic is steady. The Cluster Engine provides dedicated, single-tenant GPUs priced per GPU-hour as a Kubernetes-based Container Service, as bare metal servers with root access and RDMA-ready networking, or as managed multi-node clusters. GMI’s guide to comparing container, bare metal, and managed cluster quotes explains how to line the options up.

As of October 2026, the pricing page lists H100 from $2.60 per GPU-hour, H200 from $3.20, B200 from $5.00 with limited availability, and GB200 from $8.00, with reserved capacity and commitment-based pricing available on request. GMI’s GPU page notes that Trend Micro uses GMI Cloud GPU clusters as a more cost-effective foundation for AI workloads it previously ran on Oracle Cloud, and that Mirelo AI reports 40% lower training costs and 20% faster training. When you request a quote, ask for storage, networking, egress, and support terms in writing so the comparison table above can be completed line by line.

Conclusion

A hyperscaler vs specialized GPU cloud decision should rest on a total cost comparison, not on two hourly rates. Start with the GPU-hour, where AWS Capacity Block list prices for H100, H200, and B200 currently run about 2.1 to 2.8 times GMI Cloud’s starting rates. Then add egress, storage, support, and commitments, adjust for utilization and measured throughput, and compare cost per unit of work. For GPU-dominated workloads that run steadily, that process usually favours a specialized provider by a wide margin, while hyperscalers keep the edge where data gravity, integrated services, or committed spend already tie the workload to them. Run your own numbers through the table, test throughput on a short trial, and if the GPU line dominates your bill, start with a quote from GMI Cloud.