In today's digital landscape, companies who want to use artificial intelligence are facing a crucial question: Should they put on proven solutions such as ChatGPT or into ...
“Digitalization is not an IT project—it is a business strategy.”
– Björn Groenewold, Managing Director, Groenewold IT Solutions
> Key Takeaway: ChatGPT (via API) offers instant access without infrastructure effort, but sensitive data leaves the company.
Custom LLMs (e.g., based on Llama) provide full data control and customizability but require GPU infrastructure and ML expertise.
The choice depends on data privacy requirements, budget, and desired customization depth.
In today's digital landscape companies who want to use artificial intelligence are facing a crucial question: Should they rely on proven solutions such as ChatGPT or invest in the development of their own, company-specific Large Language Models (LLM)? Both approaches offer unique advantages and challenges.
This article highlights the most important aspects to make your decision easier.
The tempting simplicity of ChatGPT
Short: ChatGPT, developed by OpenAI, has conquered the world by storm and offers an impressively powerful and immediately ready-to-use solution for a variety of applications.
ChatGPT, developed by OpenAI, has conquered the world by storm and offers an impressively powerful and immediately ready-to-use solution for a variety of applications.
ChatGPT integration company allows to quickly benefit from the benefits of AI without having to invest in expensive infrastructure or specialised personnel.
Advantages of using ChatGPT
- Fast implementation: Because it is an API-based solution, integration into existing systems and processes is relatively simple and fast.
- ** Low initial investment:** There are no high costs for hardware or model development. Payment is usually based on use.
- Scalability: OpenAI's infrastructure allows smooth scaling as required.
- ** Maintenance and updates:** OpenAI takes care of the continuous development and maintenance of the model.
Cons and concerns
- Data protection: The transfer of sensitive business data to an external provider may represent a significant security risk.
- ** Dependence:** Companies are dependent on price models, usage restrictions and availability of the provider.
- Restricted adjustment: The possibilities for adapting the model to specific enterprise requirements are limited.
The strategic investment in an own LLM
Short: The development of its own LLM is a much more complex process, but it offers maximum control and adaptability.
The development of its own LLM is a much more complex process, but it offers maximum control and adaptability. This approach is particularly suitable for companies with specific requirements and high data protection requirements.
Benefits of your own LLM
- Maximum control and data sovereignty: All data stay in the company, which facilitates compliance with privacy policies.
- Completed adjustment: The model can be trained and optimized exactly to the needs and the specialist language of the company.
- Independence: There is no dependency on external suppliers whose prices or business decisions.
- Competitive advantage: A tailor-made LLM can create a unique competitive advantage.
Challenges at Self-Hosting
- **High cost:**The acquisition of powerful hardware (GPUs/TPUs) and operating costs are significant.
References and further reading
Short: The following independent references complement the topics in this article:
The following independent references complement the topics in this article:
- Bitkom – German digital industry association
- German Federal Office for Information Security (BSI)
- European Commission – Digital strategy
- MDN Web Docs (Mozilla)
- W3C – World Wide Web Consortium
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About the author
Managing Director of Groenewold IT Solutions GmbH and Hyperspace GmbH
For over 15 years Björn Groenewold has been developing software solutions for the mid-market. He is Managing Director of Groenewold IT Solutions GmbH and Hyperspace GmbH. As founder of Groenewold IT Solutions he has successfully supported more than 250 projects – from legacy modernisation to AI integration.
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