In the era of intelligent interfaces, users have grown accustomed to instant interactions: one click to log in, one tap to post. Behind these seamless experiences lie technologies that have existed for decades. But when generative artificial intelligence enters the picture, and in particular LLMs (Large Language Models), speed is no longer guaranteed: each response can require billions of computational operations in seconds. The heaviest load occurs even earlier, during the complex training phases.

Alongside this complexity, a new generation of language models is emerging: SLMs (Small Language Models). Less known to the general public, they are becoming essential for AI that is faster, more efficient, and tailored to specific needs and niche tasks.

The limits of LLMs: powerful, but what’s the cost?

Large Language Models like GPT or PaLM have reshaped the AI landscape - but they come with significant challenges, especially in business and industrial contexts:

• Speed constraints: the most popular models generate just 40–60 tokens per second, meaning a single response can take up to 20 seconds.

• High computational cost: LLMs are extremely energy-intensive. Each interaction requires massive computing power, translating into significant costs and energy consumption.

• Limited control: LLMs are often “black boxes,” with behaviors that are hard to predict or manage, raising liability concerns. For many industries, especially healthcare, finance, and government, this level of unpredictability is simply unacceptable.

• Privacy & hosting issues: hosting an LLM in-house is extremely costly, requiring specialized staff for deployment, plus substantial software and infrastructure expenses.

The SLM solution: targeted precision, lower costs

Small Language Models were born as a response to these challenges. They aren’t designed to “know everything”, but rather to perform highly specific tasks in defined domains.

This is why they’re becoming the preferred choice for organizations looking for vertical AI solutions that can also be hosted locally.

How SLMs work

SLMs rely on two key processes:

• Supervised specialization: the model is trained on a curated set of expert-approved responses, teaching it how to operate in a specific context.

• Distillation: the model “learns” from a larger LLM, inheriting its abilities within a narrow scope. It is fed real questions and answers generated by the bigger model, which it adapts into variations. In this way, the SLM absorbs the logic, probabilities, and nuances of the parent model.

The quality of data is crucial: the smaller the model, the more sensitive it is to training quality. This demands deep expertise in data augmentation, a wide variety of examples, and rigorous post-processing to prevent hallucinations or erroneous outputs.

Why choose SLMs? Five clear advantages

Why would a company, especially an SME or an organization with a strong vertical focus, choose SLMs instead of the more mainstream LLMs?

We’ve mapped five major competitive advantages:

• Efficiency & lower costs: SLMs are up to 7× cheaper to run compared to LLMs via API. They require fewer resources and consume less energy.

• Speed: they respond up to 10× faster than commercial LLMs, crucial for real-time applications or automated customer service.

• Accuracy: in niche domains, a well-trained SLM is often 3× more accurate than a general-purpose LLM: fewer vague answers, more reliable solutions.

• Control & scalability: being smaller, SLMs are easier to monitor, update, and manage. They can be scaled for specific use cases without relying on massive, generalist models.

• Privacy & local hosting: SLMs can be hosted on-premises, giving companies full data control. For industries bound by strict regulations, banking, public administration, healthcare, this is a major strategic asset. For Swiss companies in particular, keeping data within national borders is essential, and hosting SLMs locally makes this feasible.

How Artificialy uses SLMs

At Artificialy, we believe in sustainable, targeted, and effective AI.

We’ve leveraged Small Language Models, for example, to develop the voice-based AbaClik solution, in order to:

• Automate responses in specialized domains, with models designed ad hoc for specific clients;

• Deliver fast, seamless user experiences, even on devices with limited resources;

• Guarantee privacy by design, ensuring solutions remain entirely under client control without sending data to third-party services;

• Improve chatbot accuracy, reducing false positives and boosting the rate of correct responses.

Our approach combines the efficiency of small models with the intelligence of large ones - but only where needed.

We know when an LLM is the right fit, and when an SLM is the smarter choice. And we have the expertise to make inference fast, efficient, and scalable.

LLMs have enabled the massive, “democratic” use of AI. But today, true innovation means finding tailored solutions - and in this sense, Small Language Models represent a paradigm shift: from “all-knowing” systems to specialized, efficient, and controllable AI.