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As more organizations experiment with GenAI, the landscape of emerging AI models only continues to expand. The sheer variety of models available means that organizations that have overcome the initial question of whether they should be using AI in the first place are now faced with an even trickier question: which model should they use?
With the overwhelming number of options available in the market and new challenger models being developed and rolled out all the time, many companies are unsure which direction to take and which model to adopt to best support their application development. As we look to the future and expect more models and versions to be introduced, organizations must take an agile approach to selecting AI models. Shifting the focus from finding the most suitable single vendor to adopting a balanced, future-proof approach with LLM Mesh.
Director of Sales Engineering at Dataiku.
The risks associated with reliance on a single supplier
Relying on a single model alone is risky. For example, suppose a company centers its commercial healthcare applications around a single AI model without integrating other models. The risk is that the single model it relies on could sometimes produce inaccurate results and recommendations, leading not only to potential financial problems but also to a loss of trust in the company from the broader market. How do we know this is true? Because this is what happened to IBM, which centered its healthcare applications around the Watson AI model. Because the model sometimes provided inaccurate information, it resulted in an erosion of trust, as well as a major negative impact on its reputation. The company’s healthcare arm has struggled to recover ever since.
Despite the prominence of tools like Open AI’s ChatGPT, concerns about their governance have raised questions and doubts among investors and those involved in the integration of new technologies. Similar to IBM, there is operational risk when companies jump on a single wave and lock into a single AI model. To mitigate this risk, avoiding single vendor lock-in is crucial to navigating the rapidly changing AI landscape and alleviating concerns over security, ethics, and stability. Therefore, companies are encouraged to shift their perspective from single vendor lock-in to now jumping on all the different AI waves – using the LLM Mesh.
LLM Mesh: Jump on all waves
With the LLM Mesh, companies can ride the wave of AI models and prepare for future changes. By removing the complexity of backend connections and API requirements, the LLM Mesh makes it easy to quickly switch from one model to another, or ‘wave hop’.
The advantage of wave hopping is that companies can develop business applications using today’s best AI models, while still having the choice to move to other models. This can be done by switching to more suitable models now or by keeping options open for new models that come to market.
While companies are making informed decisions about the cost of running LLMs, which can be quite expensive, they also need to choose the right model for an application’s performance needs. By keeping options open to consider these needs, such as cost, performance, and security, companies can take advantage of a rapidly changing landscape.
The need to jump now
Why take the plunge now? Nearly 90% of executives consider GenAI a top technology priority. Waiting for the perfect wave is a competitive disadvantage strategy. While companies are looking to the future of AI technology, it’s important not to wait to jump on the AI wave if they want to avoid being left behind. To capitalize on the momentum, companies must fully immerse themselves in the use of AI. As of 2024, there will be over 125 commercial LLM models available, with a rapid increase of 120% in models released from 2022 to 2023. The landscape is growing and new emerging models are being introduced to the market – there’s no better time than now for companies to jump on the wave.
The bottom line is that for organizations looking to ride the GenAI wave without the downsides of vendor lock-in, there really is only one option: adopt an LLM Mesh approach. Not only does this approach provide the flexibility to choose which model best suits an organization’s priorities, it will also help future-proof AI applications and projects, ensuring that a company can always benefit from the latest AI models. By riding the AI wave in a smarter, more agile way, an organization has a much better chance of staying ahead of the competition and riding the tidal wave of AI innovation.
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