Introduction: The Rise of Large Language Models

Large Language Models (LLMs) have rapidly entered the mainstream. In my own experience, LLMs have become indispensable in coding, especially when using it as a pair programmer within your favorite IDE, where it helps with everything from writing code to refactoring and documentation. 

I also recently finished conducting research at the municipality of Amsterdam that explored how employees are adopting ChatAmsterdam (their own LLM-powered chatbot) for everyday tasks. It showed that municipal staff are increasingly using these models for summarizing lengthy documents, drafting and templating emails, improving writing clarity, and rephrasing texts to make them easier to understand. 

Thus, the business value of LLMs is becoming more and more evident across practically all sectors. But while these tools are powerful, many organizations are still using public LLM platforms, which isn’t always the best long-term strategy for organizations – especially when taking into account (internal) business context and processes of your organization.

This article explores why organizations should invest in building internal LLM capabilities to fully harness their potential across business operations.

Bob Oudejans (Lead Data Scientist), the author of this blog

The Considerations of Using Public LLM Platforms

Public LLM platforms offer powerful capabilities out of the box—but organizations should approach their use thoughtfully. These models are trained on a wide range of internet data, which makes them broadly knowledgeable but not tailored to your company’s specific context. As a result, they may struggle with internal terminology, workflows, or domain-specific needs, which can limit their effectiveness in more specialized business scenarios.

Additionally, using third-party LLM services often requires sharing sensitive information with external providers. While many platforms offer strong security measures, this can still raise concerns—especially in sectors where data privacy and regulatory compliance are critical, such as healthcare, finance, or government.

Lastly, as usage grows across teams and departments, organizations may find that public LLM pricing models don’t scale as cost-effectively as expected. 

These considerations don’t mean public LLMs should be avoided, but they do highlight the need to carefully evaluate when a more tailored, secure approach is appropriate. For many organizations, that means exploring internal LLMs: solutions that bring the same generative power in-house, with greater control over data, customization, and long-term costs.

The Advantages of Internal LLMs

Using internal large language models gives organizations more control, privacy, and relevance compared to relying on public AI services. By hosting the model on-premise or in a private cloud, sensitive data stays within the company, reducing compliance risks and ensuring better security.

Internal LLMs can also be connected to proprietary business data through techniques like Retrieval-Augmented Generation, making their responses far more accurate and context-aware. This improves decision-making, accelerates workflows, and reduces the need for manual information lookup.

While initial setup requires investment, long-term costs are often lower than paying for usage-based public APIs—especially at scale. Internal models can also be fine-tuned and governed to align closely with company-specific goals, terminology, and values.

In short, internal LLMs offer smarter automation, lower risk, and better business alignment, making them a powerful foundation for enterprise AI adoption.

Building Internal LLM Architectures

1. Model Hosting: On-Premise or Private Cloud Deployment

To mitigate these downsides, more organizations are opting to deploy their own LLM architectures. This doesn’t mean training a model from scratch, which is highly unfeasible, but rather leveraging existing open-source or commercially licensed models and adapting them to internal needs.

The first major decision is to determine how to run the LLM: either hosting it yourself (usually for open-source models such as LlaMa) or integrating with a vendor-managed solution (for closed-source models such as GPT-4). Hosting gives you full control, privacy, and flexibility but requires significant infrastructure and maintenance. API-based solutions offer faster deployment and ease of use, but may limit customization and lead to higher long-term costs. The right choice depends on your organization’s priorities.

2. Knowledge Injection with Retrieval-Augmented Generation (RAG)

Even the most powerful LLM won’t know anything about your internal documentation, systems, or processes unless you provide that context. Retrieval-Augmented Generation (RAG) is the current most popular way to provide that context to the LLM, and with good reason. It augments the model’s responses by dynamically retrieving relevant information from your knowledge base and feeding them into the prompt on the spot.

Implementing RAG involves three key components: an ingestion pipeline to process internal content (like PDFs, SharePoint, or Confluence), an embedding model that transforms this data into vector representations, and a prompt orchestrator that combines the user’s query with the retrieved context. This setup enables the LLM to respond with business-specific insights based on the most relevant internal knowledge.

3. Create Interface and Internal APIs

Once the model (and possibly a RAG system) are operational, the next step is to make it accessible to employees through a user-friendly interface. Frameworks like Streamlit, React, or Open Web UI are commonly used to create internal chat interfaces. Think of this as your organization’s private version of a public LLM platform but smarter, because it includes your business context, and more secure, since everything stays within your infrastructure.

In addition to human interaction, internal LLMs can be accessed via APIs to power automation behind the scenes. Business tools and apps can use this API layer to generate email drafts, classify documents, extract key data, or perform compliance checks without requiring direct user prompts.

Levi (Senior Data Engineer) and Thomas ( Analytics Translator) discussing LLM’s

Bringing LLMs In-House: A Smarter Path Forward

Public LLMs offer a great starting point, but for organizations with long-term AI ambitions, internal LLM deployments offer stronger data security, deeper business relevance, and better cost control over time. As tooling and models become more accessible, the barriers to implementing internal LLMs are rapidly disappearing.

If you’re considering integrating AI into your business, but aren’t comfortable routing sensitive data through public platforms: you’re not alone. More and more organizations are recognizing the strategic value of internal LLMs. Models that run within your own environment, are powered by your own data, and integrate directly into your existing processes. Whether you’re looking to streamline document handling, enhance internal tools, or build intelligent automations, we can help you design and deploy a secure, business-ready LLM infrastructure tailored to your needs. 

Contact us to explore how a private LLM setup can drive real value for your organization. Securely, efficiently, and on your terms.

Thinking about integrating AI into your business—but not comfortable sending sensitive data to public LLM platforms?

If you’re exploring how to bring this into your company, we’d love to chat.

Reach out to bob@aurai.com