A deep dive into the real cost of cloud AI infrastructure and when to look for alternatives

The cloud compute bill is the new rent, and for AI companies, it’s often the single largest line item. Gartner predicts that by 2026, 50% of organizations will struggle to realize the value of their AI investments due to spiraling infrastructure costs. When you’re spinning up NVIDIA H100s for training or L4s for inference, the choice between Amazon Web Services (AWS) and Google Cloud Platform (GCP) feels like a high-stakes gamble. You aren't just picking a vendor; you're choosing a philosophy of infrastructure management.

While AWS offers the sheer brute force of the P5 instances and the maturity of the EC2 ecosystem, GCP counters with the developer-friendly GKE and deep TPU integration. But the sticker price per hour is rarely what you end up paying. According to Backblaze, hidden fees like data egress and storage API calls can inflate cloud bills by up to 40%. In this guide, we’ll dissect the real differences between AWS and GCP for GPU workloads—and explore why a third option might be the smartest move for your team.

The Hardware Landscape: What Are You Actually Renting?

Before we talk dollars, let's talk silicon. Both AWS and GCP have raced to secure allocations of NVIDIA's latest chips, but their packaging differs significantly. AWS's flagship offering is the P5 instance, powered by NVIDIA H100 Tensor Core GPUs. These beasts are designed for massive scale, typically deployed in "UltraClusters" that can scale up to 20,000 GPUs. They use AWS's proprietary Elastic Fabric Adapter (EFA) networking to minimize latency during distributed training.

On the other side, Google Cloud offers the A3 mega-instance, also built on H100s. Google differentiates itself by offering a more heterogeneous mix of accelerators, including their custom Tensor Processing Units (TPUs) and the L4 GPU for efficient inference. NVIDIA notes [https://www.nvidia.com/en-us/data-center/google-cloud-platform/] that the L4 is a universal accelerator, ideal for video decoding and smaller model inference, a niche where GCP currently has better availability and pricing flexibility than AWS.

You might be wondering about availability. In 2024 and 2025, the "GPU shortage" has shifted from a total drought to a regional game of whack-a-mole. Verda's 2025 report highlights that while AWS often has more total capacity, it is frequently locked behind long-term enterprise agreements. GCP tends to have more sporadic spot availability, but their "dynamic" stock can be harder to predict for production workloads.

The Price of Power: A Deep Dive into Compute Costs

Pricing is where the battle between AWS and GCP gets complicated. It is never as simple as comparing hourly rates, because the "effective" price depends entirely on how you commit. DoiT International explains that AWS uses Savings Plans, which are monetary commitments (e.g., "I promise to spend $100/hour"), whereas GCP uses Committed Use Discounts (CUDs), which are often resource-based (e.g., "I promise to use 8 H100 chips").

Let's look at the numbers. For on-demand usage, recent market analysis suggests that AWS P5 instances (8x H100) list for approximately $98/hour, though recent price cuts have brought effective rates down for large contracts. GCP's A3 instances often list slightly lower for on-demand, around $88/hour for a similar 8-chip configuration. However, the real story is in the spot market.

Spot Instance Volatility

If you are training models, you want to use Spot instances (AWS) or Spot VMs (GCP) to save up to 90%. But here is the catch: Community discussions on Reddit reveal that AWS spot prices are highly dynamic, changing multiple times a day based on supply and demand. GCP's spot prices, by contrast, tend to be more static, often updating on a 30-day cycle. This makes GCP spot costs more predictable, but AWS can offer deeper discounts if you have the automation to chase the cheapest regions aggressively.

The Commitment Trap

For long-term workloads, you must commit. ProsperOps data indicates that AWS Savings Plans are generally more flexible. If you commit to $50/hour of compute, you can switch from P4 to P5 instances without breaking the contract. GCP's resource-based CUDs are more rigid; if you commit to A100s and want to switch to H100s six months later, you might be stuck paying for the older hardware. This rigidity is a critical risk factor for AI startups where model architectures change faster than 1-year or 3-year contracts expire.

Here is a simplified comparison of the commitment models:

AWS Savings Plans: High flexibility, applies to Fargate/Lambda, complex to calculate optimal commit.GCP CUDs: Lower flexibility (often hardware-specific), simpler flat-rate discount structure, no upfront payment required options available.Spot Availability: AWS has more frequent interruptions but deeper pools; GCP has predictable pricing but aggressive preemption (hard 24-hour limits on some older types).

Ultimately, CloudZero advises that unless you have a dedicated FinOps team managing these commitments, you will likely overpay by 20-30% on either platform due to unutilized committed capacity.

The Hidden Costs That Bleed Your Budget

The hourly rate for a GPU is just the tip of the iceberg. The bulk of your "cloud bill shock" comes from the supporting infrastructure—networking, storage, and data transfer. These are the hidden taxes of the hyperscalers. Backblaze's 2025 report found that 55% of IT leaders cite egress fees as the biggest barrier to moving data, creating a "walled garden" effect.

The Egress Tax

Both AWS and GCP charge exorbitant fees to move data out of their networks. Holori's analysis shows that after a small free tier (usually 100GB), you pay between $0.08 and $0.12 per GB for internet egress. If you are training a model on 50TB of data and need to move that dataset to a different region or a partner's facility, you could be hit with a $4,000+ bill just for the transfer. For AI companies serving models to users worldwide, these bandwidth costs can sometimes exceed the compute costs.

The NAT Gateway Trap

This is the silent killer for ML workloads. To download datasets or install packages securely, your private GPU instances need internet access via a NAT Gateway. Northflank compares the costs: AWS charges ~$0.045/hour per gateway plus $0.045 per GB of data processed. If you download a 10TB dataset through a NAT Gateway, you pay ~$450 just for the privilege of downloading your own data. GCP's Cloud NAT has a similar pricing structure. You might think, "I'll just give them public IPs," but that opens a massive security hole.

Storage API Costs

Object storage (S3 or Google Cloud Storage) seems cheap at ~$0.023/GB, but AI training involves millions of small file requests. TechVZero warns that the cost of GET and PUT requests can be shocking. Training a vision model that reads millions of images per epoch can generate thousands of dollars in API fees alone, a cost that is often completely overlooked in budget forecasts.

When you add up egress, NAT processing, and storage operations, the "effective" cost of your GPU workload is often 40-50% higher than the raw compute price. This is why many teams are looking for alternatives that offer flat-rate or bandwidth-inclusive pricing.

DevOps Overhead: The Complexity of EKS vs GKE

If pricing is the first hurdle, complexity is the second. Managing GPU workloads on hyperscalers is not a "click and run" experience; it is a full-time engineering job. The industry standard for orchestration is Kubernetes, but the implementation differs vastly between AWS and GCP.

GKE: The Kubernetes Native

Google created Kubernetes, and it shows. Sedai.io's comparison highlights that Google Kubernetes Engine (GKE) is generally more user-friendly. GKE Autopilot abstracts away much of the node management, and the "Standard" mode offers a free control plane for the first zonal cluster. GKE handles GPU driver installation and version compatibility relatively smoothly. If you are a team of developers with limited ops experience, GKE is often the safer bet.

EKS: The Manual Transmission

Amazon Elastic Kubernetes Service (EKS) is powerful but demands respect—and manual labor. Qovery notes that EKS charges $0.10/hour per cluster for the control plane, a cost that adds up if you run multiple dev/test environments. More importantly, setting up GPU nodes on EKS often requires manually configuring the NVIDIA device plugin, managing the VPC CNI plugin for networking, and handling OS-level driver updates. While AWS has improved this with Managed Node Groups, it still feels like assembling IKEA furniture without the instructions compared to GKE's polished experience.

The Hidden DevOps Cost

The real cost here isn't the software license; it's the salary of the DevOps engineer you need to hire. Dev.to community discussions frequently mention that maintaining a production-grade EKS cluster for ML workloads requires at least one dedicated engineer. If that engineer costs $150,000/year, that's an additional $12,500/month of "overhead" on your GPU cluster. For a startup or a lean enterprise team, this overhead destroys the economics of the project.

You also need to consider the "IAM tax." AWS IAM is notoriously complex. Configuring least-privilege access for a data scientist to launch a training job without accidentally deleting the production database is a non-trivial task. GCP's IAM is slightly more intuitive but still requires a steep learning curve. This complexity is why "Shadow IT" exists—data scientists get frustrated with the platform and start swiping credit cards on simpler providers.

Managed Services: SageMaker vs Vertex AI

To escape the Kubernetes complexity, both providers offer managed ML platforms: AWS SageMaker and GCP Vertex AI. These services promise to handle the infrastructure so you can focus on the code.

AWS SageMaker is a comprehensive beast. It offers everything from labeling to deployment. UUUSoftware notes that SageMaker's integration with the AWS ecosystem is unmatched. However, it comes with a premium markup over raw EC2 costs, and debugging "black box" failures in managed training jobs can be infuriating.

GCP Vertex AI is often praised for its cleaner interface and better integration with open-source tools. Compute Prices data suggests that Vertex AI can be more cost-effective for experimentation due to faster spin-up times and per-second billing. However, both platforms lock you into their proprietary workflows. Once you build your pipeline around SageMaker SDKs, migrating away is a painful refactoring process.

You have to ask yourself: Do you want to build your company's IP on a proprietary platform that charges a 30% premium, or do you want standard, portable compute?

When to Choose Which (And When to Choose Neither)

So, how do you decide? Here is a simple decision matrix based on our research:

Choose AWS if: You need massive scale (1000+ GPUs), you are already deep in the AWS ecosystem (S3/Redshift), or you need the absolute lowest latency networking (EFA) for training billion-parameter models.Choose GCP if: You prefer Kubernetes (GKE), you want easier access to TPUs, or you need more flexible spot pricing and a lower DevOps burden.Choose Neither if: You are a startup or SME with limited DevOps resources, you want to avoid egress fees, or you simply need to run Python code on a GPU without configuring a VPC, NAT Gateway, and IAM roles.

For many teams, the "Hyperscale Tax"—both in money and time—simply isn't worth it. Developers on Reddit are increasingly pointing toward specialized cloud providers that strip away the complexity.

The Alternative: Why Lyceum Technologies Makes Sense

If you are tired of debugging Kubernetes YAML files instead of training models, Lyceum Technologies offers a compelling alternative. Unlike the hyperscalers, Lyceum is built specifically for teams that want to skip the DevOps. You don't need to manage clusters, configure NAT gateways, or worry about egress fees.

Lyceum provides a "serverless-like" experience for GPUs. You authenticate, choose your hardware (from L4s to H100s), and run your code. PeerSpot comparisons highlight Lyceum's focus on ease of use and transparent pricing. Crucially, for European companies, Lyceum offers a 100% EU-sovereign cloud, ensuring GDPR compliance without the legal gymnastics required for US-based hyperscalers.

By removing the hidden costs of egress and the salary costs of DevOps management, Lyceum often delivers a Total Cost of Ownership (TCO) that is significantly lower than AWS or GCP, even if the raw hourly rate looks similar on paper. It is the difference between renting a car (Lyceum) and buying a car parts kit that you have to assemble yourself (AWS/GCP).

Key Takeaways

• AWS offers superior scale for massive clusters, but GCP provides a more user-friendly Kubernetes (GKE) experience.

• Hidden costs like data egress ($0.09/GB) and NAT gateways can inflate your cloud bill by 40% on both hyperscalers.

• Lyceum Technologies eliminates DevOps overhead and hidden fees, offering a simpler, EU-sovereign alternative for ML teams.