Pre-Trained vs Custom LLM: Which One is Your Competitive Advantage?

The software landscape is undergoing a seismic shift as AI-powered development takes center stage – an undeniable fact. GitHub reports 92% of users delving into AI tools, intensely focusing on large language models (LLMs), and a remarkable 250% surge in AI-driven projects since 2023.

For example, Microsoft offers a wealth of capabilities and services to help ISV vendors customize and extend AI experiences for users, exemplified by the Microsoft Copilot Platform.

This robust out-of-the-box platform, built on LLMs, provides a solid foundation to engage your end users with AI-powered solutions. But before you dive in, consider this:

  • Does the accuracy of your models significantly impact your product?
  • Are you committed to a long-term strategy with your products?
  • Does developmental control matter in the monetization of your product?

Being aware of limitations, relying on strengths

There are some limitations you may face when integrating LLMs into your products. Exploring and leveraging infrastructure and capabilities like those offered by Microsoft is crucial for successful AI-based solutions.

Let’s identify areas where you may lose a competitive advantage by solely relying on pre-trained models and explain why training your own models allows you more control over the outcome, all while still exploring essential Microsoft offerings.

Where do LLMs get their knowledge?

The main question is: Do you know which data LLMs are trained on?

OpenAI does not disclose the datasets used to train the models behind its popular chatbot, ChatGPT.

Microsoft states that LLMs are trained on vast amounts of publicly available data, including books, articles, and websites, to grasp language, context, and meaning.

A recent study investigated Google’s C4 dataset, which encompasses 15 million websites, spanning news outlets, personal blogs, business and tech platforms, religious pages, and community forums.

Despite this diversity, the criteria and process for selecting websites for the dataset remain undisclosed.

What we know for sure is that LLMs are trained on freely accessible, open-source data from the internet. However, they do not incorporate sensitive information such as pharmaceutical or heavy industrial data protected by nondisclosure agreements. Consequently, they lack insight into proprietary or confidential data, such as intellectual property (IP), especially in sectors like manufacturing, healthcare, or finance.

In essence: LLMs are trained on highly generic data and lack specific industry data.

You can’t sell what you don’t control

The main question is: Do you control your own weights?

When it comes to your product, ownership and control are crucial. Large Language Models (LLMs) are already complex entities, often seen as black boxes. If you’re training a model that belongs to someone else, rather than one you own or control, won’t you risk losing the ability to advance? A pre-trained approach leaves you vulnerable to the limitations and potential biases of the provider’s solution, ultimately compromising your competitive advantage and long-term success in the market.

In LLM training, getting the right answers really matters. When you use machine learning in your solutions, to stay ahead, you should aim for an accuracy score of 95 to 99%, with at least 90% as a minimum. Out-of-the-box training with basic data only gets you to 70%. That might be okay at the moment, for testing purposes, or when building a proof of concept (POC), but it’s not reliable or competitive. Especially in the long term, when your competitors use their own LLMs, your product must meet high standards. As more users expect top-notch performance, you must offer excellent user experiences and efficient software to stay competitive.

In essence: Without a clear understanding of the training data, a meticulously crafted training process for LLMs, and oversight over its outcomes, you’re essentially offering a product with at least a 30% black box experience. That poses a significant risk of losing customers.

Transparency and trust

Main question: Do you trust those who compute your data?

Remember the early days of cloud computing? Trust was a significant concern then, especially regarding data privacy and security. With traditional cloud services, trust revolved around whether service providers handled our data securely and confidentially.

However, trust goes beyond data security in the context of computing data. It includes control over how data is processed and analyzed and transparency in computational processes, meaning which conclusions they will suggest to us.

LLM providers try to address both aspects of trust in data processing by offering the option to build and manage their own data architecture within their platform. This is very convenient and effective in terms of control over computational resources and ease of use. However, it’s not helpful in controlling the input-output processes and training machine learning models.

Let’s assume you’re a service provider within the aviation business. In scenarios where LLMs are used to streamline ticket pricing, availability, and booking processes, pre-trained models may suffice. While your team may not fully grasp the intricacies of model training, access to potentially generic information can yield satisfactory outcomes.

However, in domains like aviation manufacturing, providing predictive maintenance, and quality assurance solutions, you need to know what gets into your training engine and what’s coming out of it. Transparency and trust, in this case, are directly linked to safety and security. You simply can’t rely on somebody else’s training because you know they’ll train your model on generic data.

Intellectual property

Additionally, in the context of trust, depending on external providers for your LLM solutions means giving up ownership of the model’s intellectual property. This reliance on third-party technology comes with significant risks, especially in competitive industries where maintaining a technological edge and protecting proprietary knowledge is vital for long-term success and standing out from the competition.

In essence: The crux of the issue with managed services lies in the lack of control over critical aspects such as data input-output mechanisms and model training procedures.


Main question: Will the product you’re selling to your customers be consistent now and in the long run?

Of course, you can try relying on models trained by others without having control over the training process. Pre-built instruments are attractive as someone has already done all the heavy lifting in engineering. Those models will provide you with the results we highlighted above. However, what’s important is that while the models may produce desired outcomes initially, consistency is not guaranteed. Moreover, models can quickly become outdated as the AI landscape evolves, and variations in underlying algorithms can lead to inconsistent performance.

Let’s tap into the financial institutions’ experience. A bank may use a third-party provider for risk management analytics. However, if the provider’s models and algorithms are not transparent or customizable, the bank may face challenges in accurately assessing and managing risks across different products and portfolios. This inconsistency in risk assessment can expose the bank to unexpected losses and regulatory scrutiny.

In essence: It’s not advisable to monetize a model if you lack control over its training process in the moment and have no insight into how it will evolve over time. Chances are high that in five years, you’ll face the same problems with the consistency and accuracy of your output.


Main question: Is adjusting the pre-trained model enough?

But isn’t fine-tuning a sound approach to improve a model’s knowledge, you might say.

Fine-tuning means adjusting pre-trained models to fit a specific dataset, helping the model focus on the task at hand. It involves training the model with your data while keeping the original structure of the model intact.

But let’s single out the terms of this statement that are at the core of the issue. ‘Pre-trained models’ and ‘keeping original models intact.’ It implies that the core issue remains.

While this approach offers some level of customization, allowing users to adjust the output based on specific inputs, you still lack control. You don’t know what data the model was originally trained on, before you decided to adjust it. Because you’re fine-tuning what is already trained.

For instance, in verticals like law, where specialized training data could significantly enhance model performance, users cannot input their unique datasets into the model. This limitation raises a fundamental question for those monetizing their platforms: Are you truly in control of the output you and your customers will rely on?

In essence: While fine-tuning adjusts what’s already trained, it still lacks input-output control.

Customization as a rational alternative

You might have already anticipated that I’ll be advocating for developing custom AI models tailored to your unique business needs. I firmly believe that by harnessing advanced open-source models and refining them with your proprietary data and expertise, you can attain more accuracy, efficiency, and deliver better user experience in your applications. Rest assured, I’ll provide more reasoning and real-life examples from the market, highlighting how others are already leveraging custom AI solutions to maintain their competitive edge. Remember the importance of staying ahead in today’s fast-paced landscape?

Maximizing cost efficiency

Working with robust, pre-trained open-source models enables significant cost reductions. This bypasses the substantial upfront investment required for training models from scratch, including computational resources and data acquisition costs.

Moreover, the strategic advantage of customizing and fine-tuning these models with proprietary data and unique business insights ensures accuracy, efficiency, and a delightful user experience. That’s exactly what you need to achieve a competitive edge.

Finally, while off-the-shelf services may initially seem convenient, repeatedly deploying them can lead to substantial expenses over time. In contrast, customized models offer consistent cost reductions and contribute to sustainability by consuming significantly less energy than broad, general-purpose solutions.

Use case

A leading manufacturer in the automotive sector sought to optimize its quality control processes by implementing a custom AI model. This model, trained on specific product designs, manufacturing workflows, and historical defect data, enabled the manufacturer to develop a highly specialized defect detection system. Integrated with sensors, cameras, and IoT devices on the production line, the AI model analyzed real-time data to identify potential issues with unprecedented accuracy and speed.

By detecting defects early, the manufacturer significantly reduced scrap rates, minimized rework, and enhanced overall product quality, improving efficiency and cost savings throughout production.

Far-reaching business benefits

By owning the core elements – the model itself, its weights, and the associated methodologies and data – you establish a valuable piece of intellectual property that sets you apart from competitors.

This ownership affords you the flexibility to adapt and innovate swiftly. Unlike relying on external providers, which may limit your roadmap, owning custom models empowers you to tailor your AI capabilities precisely to your business objectives and market dynamics.

Use case

Custom models can open new revenue streams, such as offering white-labeled AI solutions to industry peers. Commercializing your proprietary technology recovers development costs and positions you as an industry leader. This path is crucial to affirming your market position and sustaining long-term success.

Collaborative success

The journey towards creating custom AI models demands significant dedication, expertise, and resources. It’s a strategic decision that necessitates a careful evaluation of the value obtained from off-the-shelf solutions versus the sustainability for your business.

To simplify and enhance this process, partnering with a seasoned technology partner and making use of established AI ecosystems like Microsoft’s can prove instrumental.

By employing their state-of-the-art tools and platforms, businesses can accelerate development cycles, reduce costs, and ensure adherence to best practices from data preprocessing to model deployment. Moreover, tapping into Microsoft’s extensive knowledge of industry-specific applications and compliance standards adds further value.

A seasoned technology partner, well-versed in the Microsoft ecosystem like Unicsoft, can provide comprehensive support, including data gathering and refining, model architecture optimization, fine-tuning methodologies, and strategic deployment tactics.

Use case

Imagine an ISV partner specializing in ERP software for the retail industry. Partnering with an ISV, a retail enterprise integrated LLMs into its ERP platform to enhance customer support. The retailer improved response times and satisfaction rates by training the AI assistant on product catalogs and customer data.

Furthermore, using LLMs for demand forecasting enabled the retailer to anticipate market trends and optimize inventory levels, leading to reduced stockouts and increased revenue. Personalized marketing campaigns driven by LLM customer data analysis enhanced engagement and loyalty. Additionally, AI-powered product categorization and inventory management streamlined operations, improving user experience and efficiency while minimizing costs.

Bottom line

Working with pre-trained LLMs reveals a crucial aspect: while it demands considerable ML expertise, building your own pre-trained LLMs presents a potent alternative to off-the-shelf solutions. This becomes your distinct competitive edge, especially when you leverage the full potential of existing models.

Let’s start the conversation. I’m here to address all your questions and tell you more about the Unicsoft experience.