Traditional vendor lock-in was manageable, if not ideal. Today's AI model dependency presents a different challenge, and yet most companies treat AI vendor lock-in as if it were business as usual. This is a mistake. While nothing about AI is business as usual, model centralization is even less so. It's a critical business risk, but one that is largely unacknowledged, so mitigation measures typically don't exist. 

"I talk to enterprises that have disaster recovery plans for every layer of their infrastructure, but almost none of them have thought about what happens if the AI model running their product goes away tomorrow," said Mike Leone, a practice director and principal analyst at Omdia. 

Perhaps this is because few can imagine a foundational AI vendor floundering, crashing or selling out, especially considering the hundreds of billions of dollars cumulatively poured into the sector. But that's not how markets work. Product cycles don't suspend themselves for hot trends. History is unsentimental: Yesterday's technology darlings become tomorrow's cautionary tales

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"The real risk is not the tool; it's how tightly organizations bind themselves to it. In the AI era, that shows up as a single point of failure hiding inside what looks like progress," said Elizabeth Ngonzi, a board member and founding chair of the Ethics & Responsible AI Committee at the American Society for AI and a human-centered AI strategist, executive advisor and adjunct assistant professor at NYU.  "Foundation models are no longer just infrastructure; they're wired into decisions, workflows and customer experiences. When pricing, behavior or availability changes, the shock can ripple across the whole product surface at once."

Where AI dependency issues lurk

In theory, portability to another model should be the most logical answer to prevent or fix model dependency -- and its implementation should be a straightforward process. 

Traditional wisdom gleaned from previous software dependency experience dictates standardizing on models, separating your business logic, and treating models as interchangeable, said Rowan O'Donoghue, chief innovation officer and co-founder of Origina, a third-party provider of enterprise software support and maintenance.

"In practice, though, that's not where the dependency shows up; it creeps in through data pipelines, proprietary features and commercial terms. If your data is tied to a vendor's format, your teams rely on features that really only exist in one ecosystem," O'Donoghue said.

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While leveraging multimodel architectures can help, that's only true if they are designed into the architecture early. "Otherwise, what happens is that one model becomes dominant and everything else is there purely for comfort," O'Donoghue said.

"In the enterprise world, this is not new. The moment a vendor controls your lifecycle, you stop owning your roadmap. AI is not changing that; it's just accelerating it," he added. 

A case study in technical dependency issues

There's a lot to consider on the technical side of model dependencies, but Bo Jun Han's firsthand experience offers important insights into the issues. Han is CTO and founder of ROSTA Lab in Taiwan, an independent AI infrastructure researcher, and a Java full-stack engineer. He runs a daily multimodel orchestration setup using over eight large language models, including Claude, Gemini, Perplexity and others, all through OpenRouter's API. 

"I've personally gone through the experience of a model getting deprecated mid-project and having to execute a live switchover without dropping ongoing workloads," Han said. 

Managing reproducibility and continuity across complex systems is something he thinks about constantly, Han added.

"AI continuity isn't academic for me, it's a business constraint," he said.

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Han uses a three-tiered setup: The application layer sends requests through a standardized proxy client. A mid-layer Python + Redis router dispatches jobs by latency and cost; Claude handles long-context work and Gemini handles rapid classification. The base layer manages API key rotation across vendors.

"In theory, this sounds clean. In practice, the hidden problems almost always show up in prompts, not infrastructure," Han said. 

Different models respond wildly differently to the same system prompt. Han discovered that Claude prefers XML-style instruction formatting, while Gemini expects JSON schemas, and the "sensitivity gap between them can exceed 300% on structured output tasks."

"A prompt that works perfectly on one model can silently produce garbage on another. Most teams don't discover this until they're already in a crisis migration," Han warned.

The second lurking problem he discovered is hallucination inconsistency in multimodel ensembles. 

"If Model A is right 90% of the time and Model B is right 70% of the time, naively aggregating their outputs doesn't give you 90%, it gives you noise," Han said. 

To address it, he had to introduce an arbitration layer that improves output reliability at the expense of greater latency -- and adds one more step to the AI continuity checklist. 

Realities of hitting a single point of failure

Zooming out, there's a broader potential issue that occurs when an enterprise continually updates to the latest AI model. Chasing specific model versions creates complexity in continuity issues that are difficult to sort out. For Nick Misner, COO at Cybrary, a cybersecurity training provider, the Pentagon's recent directive provides a useful example of this complexity in action.

"The reason it created so much disruption isn't that people lacked the right tools; it's that the AI is so deeply embedded in systems and supply chains, often in ways that aren't obvious, that untangling it quickly is nearly impossible. That's not a technology failure. That's a preparedness failure," Misner said. 

He warned against being too critical of organizations that have struggled to execute a fast model swap when a directive hits -- after all, this is new technology, and there are no obvious reflexive answers. Nevertheless, CIOs must interpret these events as the warning they are.

"If we're having the same conversation five years from now and seeing the same response, that's the real problem," Misner said.

Preparing for the unexpected

Given how few enterprises have actually built an AI continuity plan, there's quite a bit of experimentation going on and more than a few surprises along the way. 

For Han, it comes back to the underestimation of prompts over infrastructure. Enterprises may appropriately measure the time needed for engineers to change configuration files, but not for prompt archeology. 

"You can swap your API endpoints in an afternoon. Rewriting and revalidating your entire prompt library takes weeks," Han said. 

Another big surprise comes in the expense of running multimodel architectures, which "can give you resilience, but they can also give you a surprisingly large bill," Han said. He found that an 8-model ensemble can cost 400% more than a single-model setup at equivalent volume. 

Building an AI continuity plan

While your mileage may vary, there are a few key elements common to early successes in developing an AI continuity plan. Evan Glaser, co-founder at Alongside AI, a fractional AI team provider, recommends the following:

In the end, "the organizations that will navigate this well are not the ones with the most advanced models. They're the ones that treat models as replaceable parts inside a resilient system, rather than the center of their strategy," Ngonzi said.