Foundational model debates--large language models, small language models, orchestration, enterprise data and choices--are surfacing in ongoing enterprise buyer discussions. The challenge: You may need a crystal ball and architecture savvy to avoid previous mistakes such as lock-in.

In recent days, we have seen the following:

Dion Hinchcliffe: Enterprises Must Now Cultivate a Capable and Diverse AI Model Garden

Now that's a lot to talk about considering how enterprises need to plow ahead with generative AI, leverage proprietary data and pick a still-in-progress model orchestration layer without being boxed in. The dream is that enterprises will be able to swap models as they improve. The reality is that swapping models may be challenging without the right architecture.

This post first appeared in the Constellation Insight newsletter, which features bespoke content weekly and is brought to you by Hitachi Vantara.

Will enterprise software vendors use proprietary models to lock you in? Possibly. There is nothing in enterprise vendor history that would indicate they won't try to lock you in.

The crystal ball says that models are likely to be commoditized at some point. There will be a time where good enough is fine as enterprises toggle between cost, speed and accuracy. Models will be like compute instances where enterprises can simply swap them as needed.

It's too early to say that LLMs will go commodity, but there's no reason to think they won't. Should that commoditization occur platforms that can create, manage and orchestrate models will win. However, there is a boom market for models for the foreseeable future.

AWS Vice President of AI Matt Wood noted that foundation models today are "disproportionately important because things are moving so quickly." Wood said: "It's important early on with these technologies to have that choice, because nobody knows how these models are going to be used and where their sweet spot is."

Wood said that LLMs will be sustainable because they're going to be trained in terms of cost, speed and power efficiency. These models will then be stacked to create advantage.

Will these various models become a commodity?

"I think foundational models are very unlikely to get commoditized because I think that there's just there is so much utility for generative AI. There's so much opportunity," said Wood, who noted that LLMs that initially boil the AI ocean are being split into prices and sizes. "You're starting to see divergence in terms of price per capability. We're talking about task models; I can see industry focus models; I can see vertically focused models; models for RAG. There's just so much utility and that's just the baseline for where we're at today."

He added:

"I doubt these models are going to become commoditized because we haven't yet built a set of criteria that helps customers evaluate models, which is well understood and broadly distributed. If you're choosing a compute instance, you can look at the amount of memory, the number of CPUs, the number of cores and networking. You can make some determination of how that will be useful to you."

In the meantime, your architecture needs to ensure that you aren't boxed in as models leapfrog each other in capabilities. Rapid advances in LLMs mean that you’ll need to hedge your bets.

Constellation Research analyst Holger Mueller noted:

"We are only at the beginning of the foundation model era. The layering of public LLMs that are up to speed on real world developments and logically merged with industry knowledge, SaaS packaging, and functional enterprise domain specific models are  going to be crucial for gen AI success. Bridging the gap from real world aware and fitting an enterprise makes genAI workable and effective."  

Business Research Themes

Data to Decisions

Roles

Chief Information Officer

Tags

#GenerativeAI, Large Language Models (LLMs)