TPU vs GPU: AI cloud costs for Italian SMEs

What it is and how it works: Google TPUs vs Nvidia GPUs in 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.

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

Basically, TPUs rock at training big models on Google Cloud. GPUs instead offer more flexibility and portability. Therefore, the choice depends on each AI project's specific vibe.

Benefits for Italian B2B SMEs

The availability of new-generation TPUs lowers the cost per compute hour. Consequently, machine learning projects previously accessible only to large enterprises become sustainable even for SMEs. 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 tighter budgets. Plus, superior performance cuts training time down. So, the time-to-market for AI projects gets significantly shorter.

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 consolidated expertise on 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 factor, not just a technical one. We at SHM Studio we see it daily in projects with our B2B clients.

Limitations and risks to consider

Google TPUs have an important limitation: the vendor lock-in . TPU-optimized models are difficult to migrate to other clouds. Nevertheless, performance may justify this dependency in specific scenarios.

Also, not all frameworks play nicely with TPUs out of the box. PyTorch, for instance, needs some extra tweaking. On the flip side, 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 in the last year.

Another risk concerns the internal skills . Without the right tech team, management costs can totally outweigh the perks. Because of this, many Italian SMEs prefer teaming up with specialized partners for their AI infrastructure choices.

According to Gartner , 60% of AI projects fail in the scaling phase due to infrastructural problems. Therefore, preliminary technical evaluation is essential before any investment.

Concrete cases: use scenarios for Italian sectors

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

B2B Retail – 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. This is because transformer models for recommendation systems benefit from the TPU architecture. Additionally, Google Cloud offers native integrations with BigQuery and Vertex AI. As a result, 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 the 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 in choosing AI infrastructure

• Choosing hardware before the model
  Many SMEs select TPUs or GPUs without having defined the model architecture. Instead, it is necessary to start from the model's requirements to identify the optimal hardware.
• Underestimating data transfer costs
  The cost per hour of computation is only part of the total expense. In fact, data transfer between cloud regions can significantly impact the overall budget.
• Ignoring European regional availability
  Not all TPU instances are available in European regions. Therefore, those who need to comply with GDPR regulations must carefully check data localization.
• Not considering the cost of skills
  Optimizing code for TPUs requires specific skills. Consequently, hardware savings may 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 a purely technical decision. It involves budget, expertise, product roadmap, and regulatory compliance. Therefore, it requires a structured evaluation that takes all these factors into account.

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

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

Finally, our activity of SEO copywriting ensures that content produced with AI support is optimized for search engines. Therefore, the infrastructure investment translates into measurable value also on the front of organic visibility. To learn more, visit our Blog or explore the web services .

Do you want to evaluate which AI infrastructure is best suited for your SME? Contact us for a free consultation We'll analyze your requirements together and provide you with a personalized cloud cost estimate.

FAQ: frequently asked questions about TPUs, GPUs, and cloud AI 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. Additionally, GPUs are supported by a broader and more established ecosystem of frameworks.

Are Google TPUs convenient for an Italian SME?

It depends on the project type 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 needed before choosing.

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 v5s have similar pricing but with 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, specific optimizations for TPUs reduce this flexibility. Therefore, it's important to evaluate the long-term cloud strategy before investing.

Where can I find support to choose the right AI infrastructure?

You can consult the official Google Cloud and Nvidia documentation for technical details. Furthermore, relying on a specialized partner speeds up the decision-making process. We at SHM Studio offer dedicated consulting for Italian SMEs. Visit the page contacts to request a personalized analysis.