Rising AI Costs: How Italian SMEs Manage Tokens

From the euphoria of tokens to the reality of costs

In the 2023-2024 biennium, the adoption of generative artificial intelligence followed a logic of maximum acceleration. Companies experimented, integrated, and scaled. Furthermore, language model providers lowered prices to gain market share. The result was an environment where cost per token seemed a secondary concern.

However, the situation has changed radically. In 2025, many organizations discovered that their AI bills had grown exponentially. According to a recent analysis by TechCrunch, the industry has experienced a true breaking point. The phrase circulating within the technical teams is emblematic: “The entire conversation shifted from tokenmaxxing and ‘go fast’ to ‘we need guardrails, how do we control this?'.

So, 2026 is the year when the economic sustainability of AI became a strategic priority. And not just for large enterprises, but also for Italian SMEs that have integrated LLM-based tools into their operational processes.

The Numbers That Matter: How Much Does AI Really Cost for an SME

Quantifying the cost of AI for a small or medium-sized business is not simple. In fact, the expense is distributed across multiple items: platform subscriptions, API costs for tokens consumed, development hours for prompt integration and maintenance. Therefore, the actual budget is often higher than planned.

Some market estimates provide a useful picture. According to research by Gartner, By 2027, more than 40% of organizations adopting generative AI will exceed their initial budgets. As a result, proactive cost management is becoming a critical skill.

For an Italian SME with 20-100 employees, monthly costs related to AI can range from a few hundred to several thousand euros. This depends on the volume of requests, the complexity of the prompts, and the choice of model. In particular, latest-generation models like GPT-4o or Claude 3.5 have significantly higher per-token costs compared to previous models.

Besides this, there are hidden costs. For example, context tokens: every time you send a long conversation to a model, you also pay for all the previous tokens. This mechanism can intuitively multiply expenses.

Strategic Reading: Why the Problem is Structural

The problem of AI costs is not temporary. Likewise, it will not be solved by simply waiting for prices to drop. In fact, competition among providers has already compressed margins. Further price reductions will be gradual and will not compensate for the growth in usage volume.

Therefore, the issue is structural. Companies that integrate AI into their workflows tend to increase consumption over time. This phenomenon is known as Usage creepEvery new use case adds tokens, and every automation generates new requests. Consequently, without clear governance, spending grows organically and becomes difficult to control.

An analysis of Harvard Business Review It emphasizes that the most effective organizations in AI adoption are not those that spend the most. On the contrary, they are the ones that define priority use cases in advance and measure the return on each application. Therefore, strategic discipline is worth more than budget availability.

For Italian SMEs, this principle is even more relevant. Margins are often tighter. Furthermore, internal technical resources for monitoring and optimizing AI spending are limited. In summary, a methodical approach is needed, which not all companies have yet developed.

The construction site still open: governance and operational guardrails

The industry's response to the cost problem is multifaceted. First and foremost, many companies are implementing granular monitoring systems. Each API request is tracked, categorized by use case, and assigned to a cost center. This makes it possible to identify areas of waste.

Subsequently, prompts are addressed. The Prompt engineering It's not just a matter of output quality. It's also a lever for economic optimization. More precise and concise prompts generate better responses while consuming fewer tokens. Therefore, investing in this skill yields a double benefit.

Also, many organizations are re-evaluating model choices. The most powerful model is not always the right one. For simple tasks like classification, data extraction, or short text generation, lighter and less expensive models produce equivalent results. Therefore, a strategy of model routing — which assigns each task to the most suitable model — can reduce costs by 30–50% without compromising quality.

We of SHM Studio we work with SME clients on this type of optimization. The goal is not to reduce AI usage. It's to maximize the value generated for every euro spent.

Operational implications for B2B retail and professional services

The sectors most exposed to the problem of AI costs in Italy are B2B retail and professional services. Both have adopted AI tools to automate communications, generate content, and support customer service. However, they have often done so without structured governance.

For B2B retail, the most common use cases include generating product descriptions, personalizing offers, and assisting with negotiations. Each of these processes continuously consumes tokens. As a result, companies with large catalogs and intense sales cycles can accumulate significant costs.

For professional services—law firms, accountants, communication agencies—AI is used for document drafting, information summarization, and report generation. In this case, the main risk is context window overloadLong documents sent repeatedly to the model multiply costs exponentially.

Therefore, the operational implications are clear. An audit of active use cases is needed. Real cost mapping is needed. Finally, a prioritization strategy is needed to distinguish high-ROI uses from marginal ones. Companies that embark on this path today will have a significant competitive advantage in the next 18-24 months.

Tools and approaches for optimizing AI spending in 2026

On the market, there are already tools dedicated to monitoring and optimizing AI costs. Among the most used are solutions from LLM Observability such as LangSmith, Helicone, and Portkey. These tools allow you to track every API call, measure latency, and calculate the cost per output generated.

However, adopting these tools requires technical skills that many SMEs lack internally. Therefore, the role of an expert digital partner becomes crucial. An agency like SHM Studio can support the company during the audit phase, in tool selection, and in defining sustainable usage policies.

In addition to this, there are architectural approaches that structurally reduce costs. For example, the Caching for recurring queries, avoid calling the model every time. Likewise, the fine-tuning Smaller models on company-specific data can produce results comparable to large models at a fraction of the cost.

For those who manage businesses digital marketing o SEO, AI cost optimization also translates into a review of content production workflows. In particular, defining standardized prompt templates and limiting the length of contexts sent to the model are immediate, high-impact interventions.

SHM Studio's Perspective: ROI First

The debate over AI costs risks polarizing into two extreme viewpoints. On one hand, there are those who argue that AI usage must be cut to control spending. On the other hand, there are those who believe that any cost is justified by innovation. However, both positions are incorrect.

The correct perspective is that of ROI. Every AI application must be evaluated based on the value it generates. Therefore, the question is not “how much do we spend on AI?” but “how much is every euro spent on AI worth?”.

To answer this question, SMEs must develop specific metrics. For example, for content generation: cost per article produced with AI vs. cost with traditional method, and compared quality. For automated customer service: cost per ticket resolved and first-contact resolution rate. For advertising campaign managementCost per qualified lead generated with AI support.

Finally, it's important to consider opportunity costs. A company that doesn't optimize its AI spending today risks losing competitiveness to rivals that do. Therefore, optimization isn't an option. It's a strategic necessity.

Those who wish to delve deeper into these topics can explore the resources available in SHM Studio Blog or contact us directly through the Contact Us for a personalized consultation.