Some of them are detailed here:
Data Security and Privacy
Risk of Data Leakage: When using third-party services, information is transmitted outside of the internal infrastructure, which could put data security and privacy at risk.
Normative compliance: It can be difficult to comply with data protection regulations such as GDPR or HIPAA when using third-party solutions that store or process data in unknown or unsecured locations.
Cost
Pricing Model: Many third-party services have usage-based or subscription-based pricing models, which can be costly in the long run.
Hidden costs: There are often additional costs, such as fees for additional services, that can add up over time.
Flexibility and Customization
Limitations on Customization: Third-party models are typically less flexible in terms of customization, as they are designed to meet the needs of a wide range of customers.
Supplier Dependence: Switching to a different solution can be difficult and expensive once time and resources have been invested in integrating a third-party model.
Control and Intellectual Property
Lack of Control: Control over the model and how it develops or improves over time generally remains with the service provider.
Limitations on Intellectual Property: Using third-party models may limit your capabilities to create and maintain a competitive advantage in terms of intellectual property.
Performance and Scalability
Latency: Response time might be slower compared to on-premise solutions due to the time required to send data to an external service and receive a response.
Downtime Risks: Model availability and performance are subject to third-party provider infrastructure, which could result in downtime or scalability issues that are outside of your control.
Before opting for a third-party LLM model, it is crucial to evaluate these factors carefully to ensure they align with your organization's specific needs and constraints.
Normative compliance: Keeping data in-house helps us comply with data protection and privacy laws and regulations, such as GDPR, HIPAA, or any other industry or regional regulations.
Customization and Flexibility
Customization: On-premise models can be fully adjusted and customized to meet the specific needs of our organization, unlike third-party models that may offer more limited or generic functionality.
Integration with Existing Systems: On-premise models are usually easier to integrate with existing infrastructures and business systems.
Cost and Scalability
Long-Term Cost: Although the initial cost may be higher due to the need for hardware and AI experts, long-term operational costs could be lower than subscription-based or third-party pay-as-you-go models.
Controlled Scalability: We have full control over the scalability of our solution, meaning we can optimize it to meet our specific demands without relying on a third-party provider.
Intellectual Property
Technology Ownership: When developing on-premise AI models, intellectual property remains within the company, offering a competitive advantage and avoiding potential legal problems related to the use of third-party technology.
Response Time and Latency
Reduced Latency: Keeping models on-premise can offer faster response times, since data does not need to travel over the Internet to be processed on external servers.
Each of these reasons can offer a strong argument for developing on-premise AI models, especially if considered within the specific context and needs of our organization.
