TPU vs GPU: AI cloud costs for Italian SMEs

What they are and how they work: Google TPUs vs Nvidia GPUs in the AI cloud

On April 22, 2025, during Google Cloud Next, Mountain View unveiled two new next-generation TPU chips. These accelerators are designed for large-scale AI workloads. Google also confirmed continued support for Nvidia GPUs within its infrastructure.

TPUs (Tensor Processing Units) are proprietary chips developed by Google. They are optimized for the tensor operations typical of machine learning. By contrast, Nvidia GPUs are widely used general-purpose accelerators. They support frameworks like PyTorch, TensorFlow and JAX on any cloud provider.

In practice, TPUs excel at training large models on Google Cloud. GPUs, on the other hand, offer more flexibility and portability. So the right choice depends on the specific context of each AI project.

Benefits for Italian B2B SMEs

The availability of new-generation TPUs lowers the cost per compute hour. As a result, machine learning projects once within reach only of large enterprises become sustainable for SMEs too. Google has announced significant price reductions compared with previous versions.

For an Italian SME using AI services to automate processes or analyze data, this means leaner budgets. Higher performance also cuts training times. So the time-to-market of AI projects shrinks noticeably.

Nvidia GPUs, on the other hand, offer a mature, well-documented ecosystem. They are supported by AWS, Azure and Google Cloud. That makes them a safe choice for teams with solid skills in standard frameworks like PyTorch.

According to McKinsey, companies that adopt AI in a structured way gain measurable competitive advantages. Choosing the right infrastructure is therefore a strategic factor, not just a technical one. At SHM Studio, we see this every day in projects with our B2B clients.

Limitations and risks to consider

Google TPUs have one major limitation: vendor lock-in. Models optimized for TPUs are hard to migrate to other clouds. Even so, the performance can justify this dependency in specific scenarios.

Not all frameworks are natively compatible with TPUs, either. PyTorch, for example, requires additional configuration. TensorFlow and JAX, by contrast, are fully supported on Google Cloud.

Nvidia GPUs are more flexible, but costs can run high. H100 instances in the cloud, in particular, carry hefty prices for long sessions. That said, regional availability in Europe has improved considerably over the past year.

Another risk concerns in-house skills. Without an adequate technical team, management costs can outweigh the benefits. For this reason, many Italian SMEs prefer to rely on specialized partners for AI infrastructure decisions.

According to Gartner, 60% of AI projects fail at the scaling stage because of infrastructure issues. A preliminary technical assessment is therefore essential before any investment.

Real-world cases: use scenarios for Italian industries

Manufacturing – Quality control with computer vision
A metalworking company in Northern Italy wants to implement a quality control system based on computer vision. In this case, Nvidia GPUs in the cloud are the most practical choice. Computer vision frameworks like YOLO and OpenCV are optimized for CUDA, and the in-house tech team already has PyTorch experience. Migrating to TPUs would require a training investment that isn't justified.

B2B retail – Product recommendations and demand forecasting
A B2B distributor with a 50,000-SKU catalog wants to train a recommendation model. In this scenario, Google TPUs offer concrete advantages. Transformer models for recommendation systems benefit from the TPU architecture, and Google Cloud offers native integrations with BigQuery and Vertex AI. As a result, the total cost of ownership is competitive with equivalent GPU solutions.

Professional services – LLM fine-tuning for customer support
A consulting firm wants to fine-tune a language model on proprietary documentation. Here the choice depends on data volume and update frequency. For projects under 100GB of data, though, spot GPUs in the cloud tend to be cheaper. Estimating costs before choosing the infrastructure is therefore crucial. Our analyses of AI projects confirm this variability.

Most common mistakes when choosing AI infrastructure

• Choosing the hardware before the model
  Many SMEs pick TPUs or GPUs without having defined the model architecture. Instead, start from the model's requirements to identify the optimal hardware.
• Underestimating data transfer costs
  The cost per compute hour is only part of the total spend. Data transfer between cloud regions can significantly affect the overall budget.
• Ignoring European regional availability
  Not all TPU instances are available in European regions. Anyone who must comply with GDPR rules needs to check data location carefully.
• Overlooking the cost of skills
  Optimizing code for TPUs requires specific expertise. As a result, savings on hardware can be wiped out by additional development costs.
• Neglecting spot or preemptible instances
  Interruptible instances cut costs by up to 80%. However, they require fault-tolerant architectures that not every SME is ready to implement.

The role of an agency like SHM Studio

Choosing between TPUs and GPUs is not a purely technical decision. It involves budget, skills, product roadmap and regulatory compliance. It calls for a structured assessment that takes all these factors into account.

At SHM Studio, we support Italian SMEs in defining the AI strategy that best fits their growth stage. In particular, we help clients assess cloud costs, choose frameworks and integrate with existing systems. We also keep tracking how the ecosystem evolves so our recommendations stay up to date.

Our AI services work hand in hand with our digital marketing and SEO activities. That way, companies can build a consistent, scalable digital presence. For example, an AI project for content personalization connects naturally with campaigns on LinkedIn and Google Ads.

Finally, our SEO copywriting service makes sure content produced with AI support is optimized for search engines. That way, your infrastructure investment turns into measurable value for organic visibility too. To learn more, visit our blog or explore our web services.

Want to find out which AI infrastructure is the best fit for your SME? Contact us for a free consultation. Together we'll review your requirements, and we'll give you a customized cloud cost estimate.

FAQ: frequently asked questions about TPUs, GPUs and AI cloud costs

What are TPUs and how do they differ from GPUs?

TPUs are chips designed by Google specifically for machine learning operations. GPUs are general-purpose accelerators made mainly by Nvidia. As a result, TPUs excel at uniform AI workloads, while GPUs offer more flexibility. GPUs are also backed by a broader, more established ecosystem of frameworks.

Are Google TPUs a good fit for an Italian SME?

It depends on the type of project and the framework you use. TPUs make sense for large transformer models on Google Cloud. For smaller projects, or those using incompatible frameworks, however, GPUs are cheaper. So you need to assess each case individually before choosing.

What are the typical costs of AI training in the cloud?

Costs vary significantly depending on the hardware, the duration and the provider. As a general rule, H100 GPU instances on Google Cloud cost between $3 and $6 per hour. TPU v5 pricing is similar, but performance is higher on optimized workloads. Spot instances can also cut costs by up to 80% for non-critical workloads.

How do you manage the risk of vendor lock-in with TPUs?

You manage lock-in by designing your architecture with abstraction layers. For example, using frameworks like JAX gives you a degree of portability across different hardware. However, TPU-specific optimizations reduce that flexibility. So it's important to assess your long-term cloud strategy before you invest.

Where can I find support in choosing the right AI infrastructure?

You can consult the official Google Cloud and Nvidia documentation for technical details. Working with a specialized partner will also speed up your decision-making. At SHM Studio, we offer dedicated consulting for Italian SMEs. Visit the contact page to request a customized analysis.