Why Enterprises Are Moving Towards Private LLMs

The next three years are expected to be very lively for AI in business. According to recent statistics, about 92% of companies worldwide plan to increase their investments in AI during this period. And don’t forget that 78% of them are already using AI in their processes, even for the most minor tasks. For most companies, generative AI is the most suitable option for meeting their needs and requirements. And when it comes to this technology, most people typically associate it with large language models (LLMs).

If you have already thought about integrating this technology into your business, you most likely have had the dilemma of what to choose: a public or private LLM. Both types have their advantages and disadvantages, but recently, many enterprises are increasingly switching their attention to private solutions. Why? What is so attractive about such LLMs? And does your business need this approach? Let's find out.

What is a private LLM?

Most people are familiar with public LLMs, like ChatGPT by OpenAI or Gemini by Google. But there are also a number of private LLMs. Let’s have a look at what a private LLM is and how it’s different from a public LLM.

A private large language model refers to an LLM that is trained, hosted, or used in a way that provides data privacy, control, and customization. Typically, such solutions are built within an organization or on a dedicated infrastructure.

A private LLM can be:

• Self-hosted (on-premise/on a private cloud)

• Fine-tuned/trained with proprietary or sensitive data

• Access-controlled to specific users or systems

And of course, such LLMs are not accessible to the general public. Organizations, especially in industries like healthcare or finance, use them for internal tasks, like customer service automation, document processing, or coding assistance.

What makes a good private LLM? A few things, actually:

• Strong security: All data is encrypted, and no data leaves your environment.

• Efficient deployment: Scalable and optimized models can run on reasonable hardware.

• Customization: No model stays good forever, it needs to evolve with your data.

• High-quality output: It should actually be good at what it does.

Here’s a short table for you to see the difference between public and private LLMs.

Popular private LLMs

Examples of private LLMs that you can deploy within your own environments include:

• Palmyra: Writer's Palmyra family offers enterprise-grade LLMs built for critical workflows.

• LLaMA: This series, developed by Meta, provides open-weight models suitable for private deployment.

• Mistral: Their models are known for their efficiency and strong performance.

• Falcon: The open-source Falcon models are used in research and enterprise apps with a focus on data privacy.

• xGen: Salesforce's xGen models focus on long-context and research-oriented enterprise use cases.

These solutions are getting more and more popular among businesses of various sizes. But why so? There are plenty of reasons, and we are going to name the most prominent ones.

Custom-tailored to relevant domains

Generic LLMs like ChatGPT and Claude are great generalists, no doubt. However, businesses need specialists. Private LLMs can be fine-tuned on all your internal data to provide you with the most expert outputs and smarter automation. Internal documentation, product catalogs, customer interactions, terminology, you name it. Everything can be fed into the LLM to make it perform domain-specific tasks with the utmost precision. It also makes adoption faster: Internal teams trust the results more quickly when they see familiar language and logic. And because the model already understands your domain, you don’t need to babysit it with overly detailed instructions.

Better accuracy

Public LLMs are trained on huge datasets from the open internet. While that gives them impressive general knowledge, it also creates limitations for real-world business applications. Public LLMs can have trouble understanding your context, like product details or regulatory limitations. Such “misunderstandings” may result in vague responses or hallucinations (blatantly wrong answers).

And since the only data a private LLM uses is your own, the accuracy improves drastically. The model will provide you with sharper and more relevant responses. Besides, generic LLMs can be subtly biased. With a private model, you can detect and correct biases in training data and reduce errors in high-stakes contexts (like contracts or prescriptions).

Full control over everything

Public LLMs are built on someone else’s infrastructure and under their rules. Dangerous. Private LLMs give you ownership of data pipelines, model tuning, access permissions, and usage policies. No vendor lock-in, no data leaks, and no surprises. You can tune the model to align with your brand voice, workflows, and business goals. You can also choose what features to update, what functionality to freeze, and when to roll out the new versions of the model. Everything belongs to you, and you don’t have to wait for model updates or pricing shifts from the vendor.

Top-tier security

With public LLMs, you will need to send your data to someone else’s servers for processing. Sometimes, your data will even go abroad. This can be hard (even impossible) for heavily-regulated industries like healthcare, finance, or legal organisations since it creates a significant privacy risk. A private model will keep your data in-house and let you run everything inside your own infrastructure.

Besides, security isn’t just about where the data lives. It’s about who has access, what happens to it, and how it's monitored. Private LLMs give you the ability to audit literally everything and prevent unauthorized access to generative AI. With role-based access, you can limit who can do what with your model. You can also segment models, so HR data never touches sales data, and vice versa.

Competitive advantage

Many companies are experimenting with AI. Only a few are making a difference. When you build or fine-tune your own LLM, you belong in the second category. You train your private solution on your customer conversations and product usage logs, so it gives answers that only your business could give.

A private LLM tuned to your workflows can automate routine decisions and help staff resolve support tickets faster. That means lower operational costs, fewer mistakes, and higher output per employee.

Better customer experience

With all technological advancements, smart, fast, and reliable support isn't just helpful, it’s expected. And AI can help you with that. With a private LLM that knows your product, policies, tone, and past customer interactions, support becomes faster and more personalized. Smart chatbots, AI agents, and summarisation tools are just the beginning. All these technologies will help you get happier customers, less churn, and stronger relationships.

Are there any challenges?

Private LLMs do offer major advantages for businesses. However, just like everything in this world, they also come with real challenges. Here’s a breakdown of the key challenges you can face when adopting a private LLM:

• High upfront and ongoing costs: Running large models requires top-tier GPUs or TPUs, specialized servers, and serious infrastructure. It can cost tens of thousands of dollars before you’ve even put anything into production.

• Expertise requirements: You will need ML engineers, DevOps/MLOps specialists, and security experts familiar with AI infrastructure. Many companies struggle to build or hire teams with deep expertise. 

• Data preparation: LLMs are only as good as the data they’re trained on. Poor data equals poor model performance, even if the model is powerful.

• Model lifecycle management: Training or fine-tuning a model once is just the beginning. You'll need to continuously evaluate its performance and update it based on new company knowledge or product changes.

• Integration complexity: Private LLMs need to connect to internal systems, access management tools, and external APIs. This means heavy engineering work to glue all of them together without struggles.

What is the future of enterprise generative AI?

The future of private large language models (LLMs) looks promising. The technology continues to evolve, the demand for data privacy is rising, and the need for customizable AI is growing. Here's an overview of the key trends you should pay attention to when thinking about integrating a private LLM:

1. Rise of smaller models: Organizations are increasingly adopting smaller, domain-specific LLMs tailored to their unique needs.

2. Integration of multimodal capabilities: LLMs are no longer just text-based. They now enable the processing and generation of images, audio, and video. This advancement diversifies the use of AI even more.

3. Enhanced real-time reasoning: Future private LLMs are expected to use real-time data integration and reasoning capabilities so they can offer up-to-date information.

By embracing these trends, you can use the full potential of artificial intelligence.

Do you need a private LLM? Checklist

Finally, a quick checklist. Here’s how you can understand whether you need a private LLM or not:

Data privacy and security needs: 

• Do you handle sensitive, proprietary, or regulated data?

• Are you concerned about sending data to third-party APIs or public LLMs?

• Do you require on-premise or private cloud deployment for compliance reasons?

Domain-specific knowledge requirements:

• Do your use cases rely on niche, technical, or internal knowledge that general-purpose LLMs don’t handle well?

• Would fine-tuning the model on your own documents, terminology, or workflows provide better results?

• Do you need an LLM that reflects your brand tone, customer language, or internal policy?

Need for control:

• Do you want to control how the model is trained, updated, and deployed?

• Do you want to monitor, audit, and log every interaction with the model?

• Is latency/uptime a critical concern where relying on third-party servers is risky?

Strategic and competitive advantage:

• Do you view LLMs as a long-term asset that should differentiate your business?

• Are you building proprietary AI capabilities (like internal assistants, custom RAG systems)?

• Do you want to avoid vendor lock-in?

Business resources:

• Do you have (or plan to invest in) engineering or DevOps teams that can manage LLM infrastructure?

• Can you allocate resources for model tuning, evaluation, and retraining?

• Have you identified specific use cases that justify the upfront and ongoing cost?

Performance expectations:

• Are current public LLMs underperforming for your specific workflows?

• Do you need consistent, predictable outputs rather than “creative” or general-purpose results?

Final scorecard:

• 12–16 boxes checked → You’re a strong candidate for a private LLM.

• 8–11 boxes checked → It’s better to test a private LLM for just key workflows.

• 0–7 boxes checked → A public LLM will be a better choice.

Bottom line

Private LLMs are becoming increasingly popular with businesses of all sizes. They can bring a huge amount of benefits and, unlike public LLMs, will be much more helpful assistants. The only thing you need to do is to properly assess your needs and capabilities. Once it’s done, you’re good to go.