Generative Artificial Intelligence (GenAI) is a rapidly developing area of AI. Its goal is to create new content – text, images, audio, or even video – based on the vast datasets it was trained on. Increasingly, these technologies form the foundation of dedicated AI solutions designed with specific business needs in mind.

A philosophical question that has accompanied this technology for years is whether AI truly creates or merely reprocesses what already exists. In practice, modern models do not “understand” content, but generate statistically most probable new combinations based on patterns from training data. However, the fact remains that the impact of GenAI today is unprecedented.

The revolution sparked by ChatGPT

A breakthrough came in November 2022, when OpenAI released the GPT model to the world as ChatGPT. Within just five days, it reached its first one million users. At the beginning of 2025, it had 400 million weekly active users; today this number has grown to 800 million per week, and forecasts suggest it may soon reach one billion users.

This pace of adoption shows how fundamental generative AI has become for business and society – not only as a general-purpose tool, but also as a foundation for dedicated AI solutions closely tailored to the needs of organizations.

The technology behind the revolution – LLMs and transformers

The key driver of this shift is large language models (LLMs – Large Language Models), trained on billions of parameters. At their core are transformers, an architecture that enables predicting subsequent words in the context of those that came before. The number of parameters is often compared to the number of connections in the human brain – a helpful metaphor for illustrating complexity, though parameters should not be confused with biological synapses.

This architectural approach is what allows organizations to develop highly customized AI solutions aligned with their unique business context, industry, and linguistic needs.

Trend: from giants to smaller, open models

The rise of generative AI is not limited to global players. Smaller language models and open source solutions that can run locally (on-premises, on a company’s own servers) are gaining importance. A major advantage of locally run models is that they can be fine-tuned on specific data, trained on specialized context, or extended with domain knowledge, for example using RAG methods.

Gartner predicted as early as 2023 that such models would become key in the coming years. Today we can see those predictions coming true. Interestingly, even BigTech companies are investing in compact AI models that run locally. At the same time, projects tailored to specific languages are emerging, such as the Polish model Bielik and similar initiatives in German or Spanish.

Artificial Intelligence on your own servers

While cloud solutions work very well for large language models, everyday tasks, and prototyping, business deployments that require knowledge of specific business context face important limitations. For this reason, more companies are considering an alternative: deploying AI on-premises.

The main advantages of this approach:

• Protection of intellectual property and data
  The cloud is convenient, but always carries some level of risk – data is the currency of the digital age. On-premises deployments allow full control of data and keep know-how inside the organization, fully protecting the business value built over the years.
• Cost optimization
  At small scale, the cloud may be cheaper, but with large deployments, its costs rise sharply (the “Pay as You Go” model). Analyses show that an on-premises approach – especially in terms of TCO over several years – can be even 3–4 times cheaper than the cloud.
• Model personalization
  Small models can be fine-tuned on internal data and documents or combined with company knowledge using RAG. As a result, they better understand industry specifics, terminology, and company processes.
• Control over latency and availability
  Cloud services do not guarantee full reliability – for example, ChatGPT may slow down during peak hours. Local models provide full control over response time, which is crucial in real-time applications such as speech transcription.

On-premises is not the only option, but in many scenarios it proves to be a reasonable strategy. If a company:

• requires full control over its data,
• wants to avoid unpredictable cloud costs,
• plans to deploy AI in critical processes,
• wants to avoid vendor lock-in,
  
  then moving to local solutions is worth serious consideration.

If not on-premises, then perhaps a hybrid?

It is worth remembering that local and cloud solutions do not have to exclude each other. More and more organizations are choosing a hybrid model, which combines the flexibility and innovation speed offered by the cloud with full data control ensured by on-premises infrastructure. Such an approach works particularly well when implementing dedicated AI solutions with diverse requirements.

In practice, this means that the most sensitive data and processes can remain inside the company, while tasks requiring high computing power – such as model fine-tuning, training smaller models, or analyzing huge datasets – can be handled in the cloud.

This approach allows companies to:

• optimize costs,
• accelerate deployments,
• use the latest technological advancements without giving up security and independence.

For many companies, this is currently the most rational path for AI development.

Generative Artificial Intelligence is not a temporary trend, but a lasting transformation in how we work and build competitive advantage. ChatGPT sparked the revolution, but true innovation is now taking place in the area of smaller, open, and locally deployed AI models, which enable the creation of scalable, tailored AI solutions.

Companies that choose the right deployment strategy today – cloud, hybrid, or on-premises – will gain an advantage in the race for the digital future.