Private LLM vs. ChatGPT, Which is Better Right Now?

In the rapidly evolving landscape of artificial intelligence, businesses are increasingly faced with the choice between leveraging commercial AI solutions like ChatGPT and developing their own private Large Language Models (LLMs). Each option presents a unique set of benefits and challenges, impacting areas such as data privacy, continuity, security, and cost management. While commercial AI services offer convenience, they often come with hidden risks related to data exposure and dependency on external providers. On the other hand, private LLMs, though requiring a significant initial investment, promise enhanced control, customization, and long-term value. This article delves into a detailed comparison of these two approaches, highlighting the critical factors that businesses need to consider to make an informed decision.

Data Privacy

One of the primary advantages of using a private LLM (Large Language Model) over a commercial AI solution like ChatGPT is data privacy. Commercial AI providers, even though they might not explicitly use your data to train their models, still collect and analyze the information you input. This can include what questions are being asked, how often, and on what topics. Such insights can inadvertently leak sensitive information about your business’s activity, growth, and market positioning. Competitors might glean valuable insights from this aggregated data, potentially putting your strategic advantages at risk.

In contrast, a private LLM ensures that all data remains within your business’s control. By claiming a private LLM, you can offer a more secure and private experience for your clients, reinforcing trust and confidentiality. This level of privacy is especially crucial for industries handling sensitive information, such as finance, healthcare, and legal services.

Continuity & Security

Reliability is another significant consideration. Commercial AI providers, despite their robustness, are not immune to market fluctuations, service outages, or even potential collapses. Dependency on these providers can expose your business to unforeseen risks, including forced outages or disruptions.

Private LLMs, on the other hand, offer greater control over continuity and security. Since the infrastructure is managed internally or through a dedicated provider, you can mitigate risks associated with external service dependencies. This ensures uninterrupted access to AI capabilities, enhancing operational stability and customer satisfaction.

Quality of Answers

The quality of responses from private LLMs can be meticulously controlled. These models can be trained exclusively on curated datasets pertinent to your industry, ensuring that the information is accurate, up-to-date, and relevant. For instance, a private LLM in the medical field can be trained using the latest medical resources, avoiding outdated or erroneous information commonly found on the broader web.

Moreover, private LLMs can be programmed to acknowledge the limits of their knowledge, offering responses like “I don’t know, seek medical advice” when they encounter questions beyond their training scope. This ensures that users receive reliable and responsible guidance, enhancing trust in your AI solutions.

Asset Not an Expense

Investing in a private LLM transforms it from a recurring expense into a valuable business asset. Unlike commercial AI services that incur ongoing subscription costs, a private LLM can be developed, trained, and optimized over time, becoming an intellectual property asset on your balance sheet. This asset can appreciate in value as it continuously learns and adapts to your business needs, providing a long-term return on investment.

Directed Responses

Private LLMs offer the flexibility to align responses with your business preferences. They can be engineered to recommend specific products, vendors, or service providers, tailoring interactions to support your strategic goals. This directed approach ensures that the AI’s output consistently aligns with your business objectives and customer service standards.

Visible & Recognized Costs

The cost predictability of private LLMs is another advantage. With a private LLM, you can establish a clear cost structure, such as costing per response, making it easier to forecast and manage expenses. In contrast, commercial AI services often have variable pricing models, which can result in unpredictable costs over time. Every interaction with a commercial AI incurs a fee, which can quickly accumulate and become a significant, often underrepresented, expense.

Future Marketable Data

A private LLM, trained on user interactions, complaints, and concerns, becomes a valuable repository of marketable data. This data can provide insights into consumer trends, interests, and pain points, which can be leveraged for additional revenue streams. For example, insights derived from a healthcare-focused LLM can inform product development, marketing strategies, and customer engagement approaches, enhancing your competitive edge.

Additional Benefits

  1. Compliance: For businesses operating in highly regulated industries, a private LLM can be tailored to comply with specific regulatory requirements, ensuring that data handling and processing meet all necessary legal standards.
  2. Performance Optimization: Private LLMs can be optimized for performance based on the unique usage patterns of your business. This can result in faster response times and more efficient processing.
  3. Integration with Proprietary Systems: Private LLMs can be seamlessly integrated with proprietary systems and databases, ensuring that all relevant information is accessible and that the AI can provide the most accurate and contextually appropriate responses.


While commercial AI solutions like ChatGPT offer convenience, the benefits of a private LLM are compelling for businesses seeking enhanced privacy, security, and control. By investing in a private LLM, you not only safeguard sensitive information but also create a valuable business asset that can be tailored to your specific needs. The ability to manage costs predictably, ensure the quality of responses, and leverage future marketable data further underscores the strategic advantages of opting for a private LLM over a commercial alternative.

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With over 30 years senior management experience across 3 continents, Australian-born David founded Sentia AI in 2018 with a mission to change the way people work at work.
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