TPU vs GPU: AI Cloud Costs for Italian SMEs

What it is and how it works: 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. Additionally, Google confirmed continued support for Nvidia GPUs within its infrastructure.

Le Tensor Processing Unit (TPU) are proprietary chips developed by Google. They are optimized for tensor operations typical of machine learning. In contrast, the Nvidia GPU They are widely adopted 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 greater flexibility and portability. Therefore, the choice depends on the specific context of each AI project.

Advantages for Italian B2B SMEs

The availability of next-generation TPUs lowers the cost per compute hour. As a result, machine learning projects previously accessible only to large enterprises become sustainable for SMEs as well. In fact, Google has announced significant price reductions compared to previous versions.

For an Italian SME that uses AI services To automate processes or analyze data, this means lower budgets. Furthermore, superior performance reduces training times. Therefore, the time-to-market for AI projects is significantly shortened.

Nvidia GPUs, on the other hand, offer a mature and well-documented ecosystem. They are supported by AWS, Azure, and Google Cloud. Therefore, they represent a safe choice for teams with established expertise in standard frameworks like PyTorch.

According to McKinsey, ...companies that adopt AI in a structured way gain measurable competitive advantages. Therefore, choosing the right infrastructure is a strategic, not just technical, factor. We at SHM Studio We observe it daily in projects with our B2B clients.

Limitations and risks to consider

Google TPUs have a significant limitation: vendor lock-in. TPU-optimized models are difficult to migrate to other clouds. Nevertheless, performance can justify this dependency in specific scenarios.

Furthermore, not all frameworks are natively compatible with TPUs. PyTorch, for example, requires additional configurations. In contrast, TensorFlow and JAX are fully supported on Google Cloud.

Nvidia GPUs are more flexible, but costs can be high. In particular, cloud H100 instances have significant prices for extended sessions. However, regional availability in Europe has improved considerably over the last year.

Another risk concerns the Internal capabilities. 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 choices.

According to Gartner, il 60% dei progetti AI fallisce nella fase di scaling per problemi infrastrutturali. Quindi, la valutazione tecnica preliminare è essenziale prima di qualsiasi investimento.

Concrete cases: Use scenarios for Italian sectors

Manufacturing – Quality Control with Computer Vision
A metalworking company in Northern Italy wants to implement a quality control system based on artificial vision. In this case, Nvidia GPUs in the cloud are the most practical choice. In fact, computer vision frameworks like YOLO and OpenCV are optimized for CUDA. Furthermore, the internal technical team already has experience with PyTorch. Therefore, migrating to TPUs would require an unjustified investment in training.

Retail B2B – Product Recommendation and Demand Forecasting
A B2B distributor with a catalog of 50,000 SKUs wants to train a recommendation model. In this scenario, Google TPUs offer concrete advantages. In fact, transformer models for recommendation systems benefit from TPU architecture. Furthermore, Google Cloud offers native integrations with BigQuery and Vertex AI. Consequently, the total cost of ownership is competitive compared to equivalent GPU solutions.

Professional – 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. However, for projects under 100GB of data, spot GPUs on the cloud are more cost-effective. Therefore, it's crucial to estimate costs before choosing the infrastructure. Our analyses on AI projects confirm this variability.

Most common mistakes when choosing AI infrastructure

• Choose hardware before the model
  Many SMEs select TPUs or GPUs without having defined the model architecture. Instead, it's necessary to start with the model's requirements to identify the optimal hardware.
• Underestimating data transfer costs
  The cost per compute hour is only one part of the total expense. In fact, data transfer between cloud regions can significantly impact the overall budget.
• Ignore European regional availability
  Not all TPU instances are available in European regions. Therefore, those who must comply with GDPR regulations should carefully verify the location of their data.
• Do not consider the cost of skills
  Optimizing code for TPUs requires specific expertise. Consequently, hardware savings can be offset by additional development costs.
• Overlooking spot or preemptible instances
  Preemptible instances reduce costs by up to 80%. However, they require fault-tolerant architectures that not all SMEs are ready to implement.

The role of an agency like SHM Studio

The choice between TPUs and GPUs is not purely a technical decision. It involves budget, expertise, product roadmaps, and regulatory compliance. Therefore, it requires a structured evaluation that takes all these factors into account.

We of SHM Studio We support Italian SMEs in defining the most suitable AI strategy for their growth stage. Specifically, we assist clients in evaluating cloud costs, choosing frameworks, and integrating with existing systems. Furthermore, we continuously monitor the evolution of the ecosystem to update our recommendations.

Our offer of AI services integrates with the activities of digital marketing e SEO. Thus, companies can build a consistent and scalable digital presence. For example, an AI project for content personalization connects naturally with campaigns on LinkedIn e Google Ads.

Finally, our activity of SEO copywriting guarantees that content produced with AI support is optimized for search engines. Therefore, the infrastructural investment translates into measurable value also in terms of organic visibility. To learn more, visit our blog explores the web services.

Do you want to evaluate which AI infrastructure is best suited for your SME? Contact us for a free consultation. We will analyze your requirements together and provide you with 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 produced mainly by Nvidia. Therefore, TPUs excel at homogeneous AI workloads, while GPUs offer greater flexibility. Furthermore, GPUs are supported by a broader and more established ecosystem of frameworks.

Are Google TPUs suitable for an Italian SME?

It depends on the type of project and the framework used. TPUs are cost-effective for large transformer models on Google Cloud. However, for smaller projects or with incompatible frameworks, GPUs are more economical. Therefore, a case-by-case evaluation is necessary before making a choice.

What are the indicative costs for AI training on the cloud?

Costs vary significantly based on hardware, duration, and provider. Generally, H100 GPU instances on Google Cloud cost between $3 and $6 per hour. TPU v5 instances have similar pricing but offer superior performance on optimized workloads. Additionally, spot instances reduce costs by up to 80% for non-critical workloads.

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

Lock-in is managed by designing the architecture with abstraction layers. For example, using frameworks like JAX allows for some portability across different hardware. However, TPU-specific optimizations reduce this flexibility. Therefore, it's important to evaluate your long-term cloud strategy before investing.

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

You can consult the official Google Cloud and Nvidia documentation for technical details. Additionally, relying on a specialized partner accelerates the decision-making process. At SHM Studio, we offer dedicated consulting services for Italian SMEs. Visit the page contacts to request a personalized analysis.