Tech industry experts are urging IT decision-makers to be wary of AI vendor gimmicks such as free tokens, and to adopt a multi-vendor and multi-model strategy to avoid vendor lock-in.

“Don’t be afraid to adopt a multi-vendor approach to get value from different AI tools rather than risk lock-in with a single one,” said Max Goss, senior director analyst at Gartner.

It is unlikely one AI vendor or model will meet an organization’s requirements, Goss said.

The advice comes as more AI vendors are offering cheap tokens subsidized by venture capital in a land grab for customers. The companies are also hiring forward-deployed engineers (FDEs) to push their models to enterprises.

Once companies start developing business processes around specific AI models, they get locked into their ecosystem. “People are adopting hybrid strategies…to cut token costs, and adopting more token-efficient models,” said Jack Gold, principal analyst at J. Gold Associates.

Free and low-cost tokens from AI vendors could incentivize companies to build processes and workflows around proprietary LLMs and agents, said Max Leaming, head of data science and AI solutions at ManpowerGroup.

But as the AI landscape evolves, it’s difficult to predict whether a multi-model or multi-vendor landscape will emerge, said Logan Wolfe, partner of global AI strategy and sovereign transformation at IT consulting firm Kyndryl. “I think it could be multi-model, yes. It really comes down to the use case and the type of implementation that you’re having,” Wolfe said.

Enterprises are still in the midst of moving blue-sky experimentation to a mindset where they see AI as a powerful tool that needs to make sense from a business perspective. With that in mind, IT leaders should ground their AI strategies on use cases as opposed to vendors, Wolfe said.

“If it’s a highly regulated space, if it’s a financial sector, a healthcare sector, then you will be placing a lot more emphasis on safety, privacy, maintaining certain regulations, and so that could prevent you from rapid model switching based on cost,” Wolfe said.

For low-stakes use cases, it would be prudent to have a model-switching approach that doesn’t break the bank. “For a low-hanging fruit use case with varying volume, like a customer support data center, during heavy load times you could switch to the more capable model, then optimize that on evenings and weekends,” Wolfe said.

ServiceNow Chief Digital Information Officer Kellie Romack, who’s worked in IT for 25 years, said companies need to understand how their AI is built. “You can’t have AI built in such a way that you don’t have human beings understanding how it was built…, how to debug, back up, and retrace,” she said.

Romack has also long resisted ripping out one vendor’s platform to replace it with another. “I say, ‘Let’s talk about the technology you already have…, now let’s see the best of breed,’” she said.

After studying what customers already own and where their contracts and plans are headed, Romack looks at options based on architectural principles and the problem being solved, then runs multiple models in-house, such as Anthropic’s Claude and Microsoft’s Copilot, through one LLM gateway.

“We have a lot of different things in-house that people can put their fingers on,” Romack said.

For example, Claude might be better for reading a long Word document, while Copilot might be better for a quick summary.

She is sensitive about internal AI spending. “Every day we look at token spend. I’ll look at an engineer that’s got the same job as another engineer, and I’m like, ‘OK, you spent $10, you spent $10,000. Why?’”

Avoiding vendor lock-in is important for continuity of service. Outages hit AI services from OpenAI and Claude in recent months, and a multi-model approach provides fallback options, Gartner’s Goss said.

“If you are relying on a single provider with a single model, there’s risk there. You can mitigate that risk with a multi-model approach,” he said.