The end of one-model-fits-all: LLMs are going domain-specific

Would you rather hire a brilliant generalist with no industry experience—someone who performs well but lacks insight into what truly matters in your field—or an expert with hands-on experience who understands your industry specifics, pain points, and priorities and delivers targeted, effective results?

That’s the core difference between choosing general-purpose and domain-specific large language models (LLMs). Most models you’ve probably heard of—GPT, Claude, DeepSeek, and others—fall into the first category. They are trained on vast amounts of data, highly versatile, and capable of performing many tasks, but they are overly general and require additional training to deliver accurate and relevant outputs.

To achieve better contextual understanding, deeper insights, higher-quality results, and enhanced security and control, a growing number of companies are turning to domain-specific LLMs. Gartner predicts that by 2027, more than 50% of LLMs used by enterprises will be tailored for a particular industry or business function. To implement such models, business leaders apply one of the following approaches. 

The first is training the model from scratch using company specific data. This method provides complete control over model architecture and training data, superior performance on specialized tasks, and deep domain expertise. However, it is extremely resource-intensive, time-consuming, and requires large, high-quality datasets that are often challenging to gather. This approach is typically accessible to large enterprises with extensive domain-specific data and sufficient infrastructure. 

A recent revolutionary example is Stripe. In May 2025, Stripe introduced its payments foundation model, trained on tens of billions of transactions. Although it is not an LLM in the traditional sense, it works similarly. Like a language model embeds words, Stripe’s self-supervised network learns dense, general-purpose vectors for every transaction. Capturing nuanced patterns within payment data, this model significantly increased the company’s detection rate for attacks on large users—from 59% to 97% overnight.

The second, more common option, is fine-tuning existing models. Data engineers take a general-purpose LLM—like Llama, GPT, or Claude—and train it on company-specific data such as an internal knowledge base, documents, and customer interactions. Like models trained from scratch, fine-tuned LLMs are designed to address crucial domain scenarios, match the brand’s voice, tone, and guidelines, and control the data the model is exposed to. However, unlike models trained from scratch, these LLMs remain closely tied to the structure of the original pretrained model, are not easily modified at their core, and may overfit if the training data is too narrow or repetitive. 

This approach is less time-consuming and more cost-effective because it skips the most resource-intensive phase of training a model to understand general syntax, semantics, and reasoning from broad datasets. Still, the timeline can grow depending on the scale of customization, quality of training data, and complexity of tasks. 

For companies requiring fast time-to-market, leveraging out-of-the-box LLM is an attractive option. One such example is Aisera, which offers more than 25 industry-specific LLMs. These models are built with domain knowledge and can be deployed with minimal configuration. However, limited customization prevents them from fully capturing a specific business’s tone, terminology, or workflows. In sensitive and highly regulated environments, off-the-shelf solutions can fail to meet compliance standards. To address these challenges, companies often perform light fine-tuning or use techniques such as retrieval-augmented generation (RAG) to extend the model with their own data.

There is no universal path to introducing a domain-specific LLM; it requires a mix of organizational readiness and technical expertise. On the company’s side, this means having high-quality, diverse, and structured domain data, as well as clearly defined use cases. Other tasks like selecting the right model, preparing training pipelines, evaluating performance, and ensuring secure deployment can be handled by technical experts. If you are looking for support with training models, fine-tuning, and customization, our team helps with every stage of model implementation.