GenAI and LLM Approach: Build, Buy or Both?

Generative artificial intelligence (AI) and large language models (LLMs) have forced organizations across the globe to jump into the deep end of the pool, ramp up quickly as these advancements offer unprecedented opportunities for enhancing efficiency, driving innovation, and gaining a competitive edge in an increasingly data-driven world. When assessing the potential of generative AI businesses are faced with a critical decision: should they adopt external API solutions for LLMs, or invest in building proprietary in-house models? The question requires much to consider, examining the implications of each approach with a focus on cost, maintenance, information security, data privacy, strategic and organizational alignment.

Cost Considerations
The cost of integrating LLMs into business operations is a multifaceted issue that encompasses initial investment, ongoing expenses, and potential returns on investment (ROI). External API solutions offer a seemingly cost-effective pathway to leveraging the power of LLMs. These platforms enable businesses to access state-of-the-art models with pay-as-you-go pricing structures, eliminating the need for substantial upfront investment in research and development (R&D). Providers such as OpenAI, Google, and others continuously update their models, ensuring that businesses benefit from the latest advancements without additional R&D expenditure.
However, reliance on external APIs can lead to escalating costs as usage increases. While initially affordable, these solutions may become costly as businesses scale, especially if their usage patterns are unpredictable or if they require high levels of customization. Furthermore, the generic nature of external APIs may result in inefficiencies, as businesses spend on functionalities that do not precisely match their needs, potentially diluting the ROI.
By contrast, in-house LLM development will be a significant undertaking with substantial initial investment. Organizations must allocate resources to R&D, infrastructure, and talent acquisition. The cost extends beyond the model’s creation to include ongoing maintenance, updates, and security controls, measures and best practices. Despite these challenges, an in-house approach offers the potential for higher customization and alignment with business goals and objectives. The ability to fine-tune models to specific tasks or industries can result in greater effectiveness and efficiency, ultimately a stronger ROI, justifying the initial investment over time.

Maintenance and Evolution
Maintaining and evolving an LLM is crucial for ensuring its continued relevance and effectiveness. External API solutions relieve businesses of the burden of maintenance, as the service provider is responsible for updates, security patches, and infrastructure improvements. This arrangement allows businesses to focus on their core operations, leveraging the benefits of generative AI without the complexities of model management.
However, this convenience comes with a loss of control over the model’s evolution. Businesses will need to account for and set strategy to the provider’s update schedule, which may not align with organizational timelines. The in-house LLMs grants organizations complete control over the model’s development trajectory…but requires a dedicated team for ongoing maintenance and updates. While this demands additional resources, businesses can prioritize updates based on their strategic objectives, ensuring that the model evolves in tandem with their operations and market demands.

information security and data privacy
Information security and data privacy are paramount in general and more so when working with generative AI and LLMs, particularly for organizations handling sensitive data. A day doesn’t go by when there are news headlines concerning the exposure or exfiltration of sensitive or personally identifiable information. Whether through human error or at the hands of threat actors, these events continue to place data privacy at risk. Now organizations need to account for the insider threat in the form of misconfigured generative AI and LLM environments, controls, and data protection.
The use of external LLMs necessitates the sharing of data with third-party providers, which raises concerns about data privacy and the risk of breaches. While reputable providers implement robust security measures, the risk of external data processing cannot be eliminated. Moreover, regulatory compliance becomes more complex when data crosses borders or is managed by external entities.
Building an in-house LLM offers a solution to these challenges by keeping data within the organization’s control. This approach facilitates compliance with data protection regulations such as the GDPR and CCPA, as businesses can implement tailored security measures and data handling practices. Additionally, in-house models enable organizations to respond more swiftly to security threats, patch vulnerabilities, and adapt to regulatory changes, providing a higher level of data protection and privacy assurance.
There are industry and international standards that provide guidance for data privacy and information security. Both NIST and ISO have recently released the NIST AI Risk Management Framework (https://www.nist.gov/itl/ai-risk-management-framework) and ISO/IEC 42001 Standards (https://www.iso.org/standard/81230.html) that give organizations a starting point for assessing their AI security requirements and risk management, regardless of the deployment approach.

Strategic Alignment and Competitive Advantage
The decision between external and in-house LLMs is also a strategic one, influenced by an organization’s competitive landscape, industry specifics, and long-term vision. External APIs offer a quick and accessible entry point into the world of generative AI, allowing businesses to experiment with and deploy AI-driven solutions rapidly. This can be particularly beneficial for small to medium businesses or organizations at the early stages of AI adoption, providing them with the agility to respond to market changes and explore new opportunities without a significant initial investment.
Offering a bespoke solution that provides a competitive edge, an in-house LLM can serve as a strategic asset. Organizations with unique use cases, specialized knowledge, or industry-specific requirements may find that generic external models fall short of their needs. In-house LLMs, tailored to the organization’s specific context and challenges, can enhance operational efficiency, improve customer experiences, and drive innovation. Furthermore, owning and controlling a proprietary LLM can become a differentiator in the market, establishing the organization as a leader in its field.

Navigating the Decision
The choice between adopting external APIs and building in-house LLMs is complex and multifaceted. Organizations must consider their immediate needs, long-term goals, and the strategic value of generative AI within their operations. Factors such as the availability of data, expertise, and resources play a crucial role in this decision-making process.
For businesses considering an in-house approach, the questions of data readiness, expert availability, and time constraints are critical. High-quality, well-organized data is the foundation of effective LLM training, necessitating a thorough assessment of the organization’s data assets. Similarly, the availability of experts to train, test, and refine the model is essential for its success. Organizations must also evaluate their time horizon, acknowledging that developing an in-house LLM is a long-term investment that may not yield immediate returns.

Organizations must carefully weigh these factors, considering their specific circumstances, strategic objectives, and the evolving landscape of generative AI. Whether opting for the agility and ease of external APIs or the customization and control of an in-house model, businesses must remain agile, ready to adapt their strategies, leveraging the transformative power of generative AI and LLMs, driving innovation, and securing their place in the future of their industries.