Vertical vs Horizontal LLMs: Which Does Your Business Need?

A horizontal LLM is a general-purpose AI model trained on a wide mix of text, so it can help with almost anything. A vertical LLM is tuned for one industry, like finance, law or healthcare, so it knows that field's terms and rules better. Most businesses should start with a horizontal model plus their own data, and move to a vertical model only when a clear gap shows up.

Here is how to think about vertical vs horizontal LLMs without the sales pitch.

What is a horizontal LLM?

An LLM (large language model) is the AI behind tools like ChatGPT, Claude and Gemini. It learns patterns from huge amounts of text and predicts what words should come next.

"Horizontal" means it covers many subjects. The same model can write an email, summarise a contract, explain tax rules and write code.

Strengths:

• Very flexible. One model serves many teams.
• Easy to start with. You can use it through a website or an API (a way for software to call it).
• Improves fast, because big vendors release new versions often.

Weaknesses:

• It may not know your industry's jargon well.
• It knows nothing about your company unless you give it your data.
• It can sound confident when it is wrong.

What is a vertical LLM?

A vertical LLM is built or adjusted for one domain. There are two common ways:

• Train from scratch on domain data. The best-known example is BloombergGPT. Bloomberg built a 50-billion-parameter model trained on a 363-billion-token financial dataset plus 345 billion tokens of general text. A "token" is a chunk of text, roughly a word or part of one. Bloomberg reported it did much better on financial tasks than similar-sized open models.
• Fine-tune a general model. Fine-tuning means extra training on a smaller, focused dataset, such as your insurance claims or legal precedents. This is far cheaper than training from scratch.

Strengths:

• Better with specialist language, e.g. "IBNR reserves" in insurance or ICD codes in medicine.
• Can follow industry formats and conventions more reliably.

Weaknesses:

• Costly to build and maintain. Training from scratch needs large data and compute budgets.
• Can fall behind. General models get new versions often; a custom model needs re-training to keep up.
• Narrower. It may do worse outside its field.

Vertical vs horizontal LLMs: side by side

	Horizontal LLM	Vertical LLM
Knowledge	Broad	Deep in one field
Setup cost	Low	Medium (fine-tune) to very high (from scratch)
Time to start	Days	Weeks to months
Keeping current	Vendor updates it	You update it
Best for	Mixed, general tasks	Narrow, high-volume specialist tasks

The middle path most firms should take

There is a third option that often beats both: a horizontal model connected to your own documents.

This is called RAG, short for retrieval-augmented generation. The system finds relevant passages in your files and gives them to the model before it answers. The method comes from a 2020 research paper, and it is now widely used.

Why it works well for many companies:

• Your policies, product data and past cases stay in your own store, not baked into the model.
• Updating knowledge means updating documents, not retraining.
• You can switch to a newer model later without redoing the work.

Example: a Malaysian insurer wants help answering agents' questions about policy wording. A general model plus RAG over the insurer's policy documents will likely get most of the way. Fine-tuning becomes worth it only if, after testing, the model keeps misreading specialist terms.

More on RAG in RAG vs AI agents vs agentic RAG, and on the search technology behind it in vector databases explained.

When a vertical LLM makes sense

Consider going vertical when most of these are true:

• The task is narrow and repeated thousands of times, e.g. coding medical claims.
• A general model plus RAG has been tested and still falls short.
• You have a large amount of clean, labelled, domain data you are allowed to use.
• Mistakes are costly, and you can measure accuracy clearly.
• You have the budget and people to maintain the model over time.

If you can't tick most of these, stay general for now.

Data, privacy and sovereignty

Choosing a model is also choosing where your data goes.

• Training data. Fine-tuning on customer records means personal data is used for a new purpose. Under Malaysia's PDPA, check your consent and notices first, and remove what you don't need.
• Where it runs. Some sectors, like banks and healthcare, may prefer models that run in their own data centre or a local cloud. Smaller open-weight models (models whose files you can download and run yourself) make this more practical, and they are also a good base for fine-tuning.
• Governance. Whatever you pick, keep a record of what data went in, who can use the model, and how you test it. Our AI governance guide covers this.

Conclusion

The vertical vs horizontal LLMs choice is not either-or. Start with a strong general model, connect it to your own documents, and test it on real work. Move to a vertical model, by fine-tuning or buying one built for your industry, only when testing shows a clear gap that is worth the cost.

Quick answers

Is a vertical LLM always more accurate?

Only inside its field, and not always even then. A strong general model with good access to your documents can match or beat it on many tasks. Test both on your own examples.

What is the difference between fine-tuning and RAG?

Fine-tuning changes the model by training it further. RAG leaves the model alone and gives it the right documents at question time. RAG is cheaper and easier to keep current.

Can a small business build a vertical LLM?

Building one from scratch, no. Fine-tuning a small open-weight model is possible but rarely needed. Most small businesses get more value from a general model plus good documents.

Are there vertical LLMs I can buy?

Yes, many vendors sell AI tools tuned for legal, medical, finance and other fields. Ask them to prove accuracy on your own sample data before you sign.