Traditional vendor lock-in was manageable, if not ideal. Today’s AI model dependencies pose other challenges, but most companies treat AI vendor lock-in as if it’s business as usual. This is wrong. Although nothing is business as usual when it comes to AI. Centralization of models Even less so. This is a significant business risk that is largely unrecognized and for which there is usually no mitigation.
“I talk to companies that have disaster recovery plans in place for every layer of their infrastructure, but very few of them think about what would happen if the AI models running their products disappeared tomorrow,” said Mike Leone, practice director and principal analyst at Omdia.
Perhaps this is because few can imagine fundamental AI vendors struggling, going out of business, or being sold, especially given the following circumstances: hundreds of billions of dollars cumulatively poured into this sector. But markets don’t work that way. Product cycles are never paused due to trending trends. History is not sentimental: Yesterday’s technology darlings are tomorrow’s lessons
“The real risk is not the tool, but how tightly an organization is tied to that tool. In the AI era, that manifests as a single point of failure hidden in what appears to be progress,” said Elizabeth Gonzi, director and founding chair of the American Institute for AI’s Ethics and Responsible AI Committee, human-centered AI strategist, executive advisor, and adjunct assistant professor at New York University. “The underlying model is no longer just infrastructure; it is embedded in decision-making, workflow, and customer experience. When pricing, behavior, or availability changes, the impact can ripple across the entire product surface at once.”
Where the AI dependency problem lies
In theory, portability to another model should be the most logical answer to preventing or fixing model dependencies, and implementing it should be an easy process.
Traditional wisdom gathered from our ancestors Software dependencies “Experience has made it important to standardize models, separate business logic, and treat models as interchangeable,” says Rowan O’Donoghue, co-founder and chief innovation officer at Origina, a third-party provider of enterprise software support and maintenance.
“But in reality, that’s not where dependencies emerge; they creep in through data pipelines, proprietary capabilities, and commercial terms. When data is tied to a vendor format, teams are relying on functionality that actually only exists in one ecosystem,” O’Donoghue said.
Leveraging a multi-model architecture can be helpful, but only if it is built into the architecture early. “Otherwise, one model will dominate and all the others will exist solely for comfort,” O’Donoghue says.
“In the corporate world, this is nothing new. The moment a vendor manages the lifecycle, you no longer own the roadmap. AI doesn’t change it, it just accelerates it,” he added.
Technical dependency issue case study
There is a lot to consider on the technical side of model dependencies, but Bo Jun Han’s first-hand experience provides important insight into this issue. Han is the CTO and founder of ROSTA Lab in Taiwan, an independent AI infrastructure researcher, and a Java full-stack engineer. He runs multi-model orchestration setups every day using over eight large language models, including Claude, Gemini, Perplexity, and more, all through OpenRouter’s API.
“I have personally had the experience where a model becomes obsolete in the middle of a project and I have to perform a live switchover without dropping any ongoing workloads,” says Han.
Han added that managing reproducibility and continuity across complex systems is something he always thinks about.
“For me, AI continuity is a business constraint, not an academic one,” he said.
Han uses a three-tier setup. The application layer sends requests through a standardized proxy client. A middle-tier Python + Redis router dispatches jobs based on latency and cost. Claude handles long context work, Gemini handles quick categorization. The base layer manages the rotation of API keys between vendors.
“In theory, this sounds pretty; in reality, hidden problems most often show up in the prompts, not the infrastructure,” Han says.
Different models respond very differently to the same system prompts. Han found that Claude prefers an XML instruction format, while Gemini expects a JSON schema, and that “the difference in sensitivity between the two can be more than 300% for structured output tasks.”
“A prompt that works perfectly in one model can silently generate garbage in another. Most teams don’t realize this until they’re already in a crisis transition,” Han warned.
The second potential problem he discovered is illusory inconsistency in multi-model ensembles.
“If model A is correct 90% of the time and model B is correct 70% of the time, simply aggregating the outputs won’t give you 90% and will introduce noise,” Han says.
To address this, he had to introduce an arbitration layer that improves output reliability at the cost of increased latency, adding another step to the AI continuity checklist.
The reality of encountering a single point of failure
Zooming out, there are broader potential issues that arise as companies continually update to the latest AI models. Tracking versions of a particular model complicates continuity issues and makes them difficult to solve. For Nick Meisner, chief operating officer of cybersecurity training provider Cybrary, the Department of Defense’s recent directive serves as a useful example of this complexity in action.
“The reason we’ve caused such a huge disruption is not because people didn’t have the right tools. AI is so deeply embedded in systems and supply chains, often invisibly, that it’s almost impossible to unravel quickly. It’s not a failure of technology. It’s a failure of preparation,” Meisner said.
He cautioned against being too critical of organizations that struggle to carry out rapid model exchanges when mandates are issued. This is a new technology, after all, and there are no obvious knee-jerk answers. Nevertheless, CIOs should interpret these events as warnings.
“If you’re having the same conversation five years from now and you’re getting the same response, that’s the real problem,” Meisner said.
Prepare for the unexpected
Considering that very few companies have actually built an AI continuity plan, there is a fair amount of experimentation going on, and many surprises along the way.
For Han, it comes back to underestimating prompts over infrastructure. Companies can better measure the time it takes for engineers to change configuration files, but they don’t need it to do archeology quickly.
“API endpoints can be replaced in an afternoon, and rewriting and revalidating the entire prompt library will take several weeks,” says Han.
Another big surprise is the cost of running a multi-model architecture. This “gives you resiliency, but it can also lead to surprisingly large bills,” Han says. He found that an ensemble of eight models could cost 400% more than a single-model setup of comparable volume.
Build an AI continuity plan
Although the distances achieved may vary, there are some key elements common to early successes in developing AI continuity plans. Evan Glaser, co-founder of Alongside AI, a fractional AI team provider, recommends:
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Hierarchy of importance. not all AI integration carries the same risks. The model that powers internal summary tools is different from the model that goes into customer-facing underwriting decisions. By tiering integrations according to business impact, you know where to invest in redundancy first.
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Performance baseline. If you don’t know what your current model’s “tolerance” is, you can’t fail over to an alternate model. Document latency, accuracy, throughput, and output quality benchmarks for each critical integration. These are the criteria for acceptance of exchange.
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Contractual protection. Check your vendor agreement for deprecation notice periods, price change provisions, and data portability rights. Please note: Most Foundation Model API terms are surprisingly thin regarding these protections compared to traditional enterprise software agreements.
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Switching procedure. For each significant integration, document what it takes to replace the model, not in theory, but in terms of engineering time, test cycles, and revalidation efforts. That number is your true exposure.
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Continuity of governance and compliance. In a regulated industry, model switching is more than just a technical task. It’s mandatory. Once you have verified a model’s regulatory compliance, you should also go through the same verification of any replacement models. Continuation planning must account for that timeline, as it often takes longer than a technical migration.
Ultimately, “the organizations that will survive this best will not be those with cutting-edge models; they will be the ones that treat models as replaceable parts in a resilient system, rather than as the core of their strategy,” Ngonzi said.
