Keeping up with the pace of software advancement is a challenge most organizations and tech leaders know all too well. Weeks or months are spent navigating the maze of procurement, purchasing, contracting, implementation, and more, only for a new and possibly better solution to emerge shortly thereafter. That process was a fact of life for decades — and then the AI explosion arrived.
In the era of traditional software, a competitor might emerge every six months or maybe once a year, but major AI updates are happening weekly — and, in some cases, daily. In fact, almost 300 new AI model updates or LLM releases have shipped in the last 12 months alone.
If you’re wondering how any organization can be expected to keep up, the answer is simple: They can’t. As a result, organizations will likely want to ensure that they’re capable of switching vendors as new models and updates become available. But when those organizations become locked into a contract with an AI provider — known as “vendor lock-in” — making that switch and keeping pace with the speed of AI innovation can be almost impossible.
“Vendor lock-in” happens when an organization could switch to a cheaper, more effective, or less problematic technology solution, but the economics of making that switch prevent them from doing so. In the case of typical SaaS solutions, the cost of migration or implementation may be too great, the work required to get there may be too arduous, or the training needed to facilitate adoption may be too time-consuming.
Those same obstacles apply to AI procurement. But in the case of AI, vendor lock-in doesn’t just lead to overspending or user frustration — it can also create strategic liabilities. The speed of AI advancements means lengthy contracts can lock customers out of new solutions and, as Swfte puts it:
“Inflated costs quietly compound as proprietary pricing tiers escalate year over year, while innovation stalls because teams are forced to work within the boundaries of a single provider's roadmap rather than selecting the best tool for each job.”
In other words, in addition to the challenges companies experience with traditional SaaS vendor lock-in, being locked into an AI provider can be a roadblock to innovation, productivity, and even parity with competitors as models continue to advance.
Recently, one X user took to the platform to voice his dissatisfaction with Anthropic for shutting down his team’s Claude Code licenses without warning or explanation:
“Suddenly, more than 60 people were left without a fundamental tool for their work. Integrations, skills, conversation histories: all lost or, in the best-case scenario, on indefinite hold.
A huge lesson for any software company that relies on AI tools in critical processes. Never put all your eggs in one basket.”
Days later, another company reported an almost identical situation on Reddit. While Anthropic quickly reinstated Claude access for both teams, the lesson here is hard to ignore: Relying on any one AI solution — or just any one software solution — is a major liability for any organization.
Having licenses unexpectedly revoked is an extreme example, but it’s far from the only way vendor lock-in creates strategic liabilities and productivity bottlenecks.
This one’s as simple as it sounds: AI is a very new technology, relatively speaking, and companies that specialize in new technologies often go out of business. In fact, they often go out of business abruptly. Microsoft-backed Builder.ai did just that in 2025, as did a range of others, including a number of would-be enterprise AI solutions.
Imagine a scenario wherein much or all of a department, like customer support, shipping and logistics, revenue operations, or software engineering, relies on a solution from a single AI provider. If that provider goes out business, deprecates that solution, or just revokes your access — like those companies experienced with Claude above — with little to no warning, that loss of access can hamstring an entire department at best. At worst, it can result in lost revenue, missed SLAs, voided contracts, and customer churn.
OpenAI API access costs organizations an average of $384,500 annually, according to Saas and AI spend optimization platform Zylo. They also report that AI costs rose by 108% in 2025, and that 78% of IT leaders experienced unexpected charges related to AI use. Given the lengthy contracts required by many leading AI solutions, these soaring costs can lock tech leaders into relying on expensive solutions that may be outpaced with new models entering the market.
While this problem has (probably) been around for as long as contracts have existed, the speed at which AI is advancing makes it especially problematic for organizations trying to stay ahead — just on par with — the competition. In other words, tech leaders can be contractually locked into paying a premium for service that no longer warrants the price tag.
AI compliance regulations are still new and often murky, and there isn’t a great deal of clarity around when or if proposed legislation governing AI use will become law. But some laws are already in effect and more will no doubt be added soon, likely bringing the “Wild West” days of AI use to a halt. Take the EU AI Act, which passed in 2024, for example — and it doesn’t just affect companies headquartered in the EU.
Any company whose AI outputs are used in the EU must comply with the AI Act, regardless of where the company is headquartered — meaning U.S.-based AI providers are now governed by EU rules, whether they planned for it or not. It also means any resulting changes a vendor makes to become compliant with the act will likely affect you, too. There are also provisions that introduce risks for vendors that modify underlying AI models, requirements for AI literacy for employees using AI tools, documentation requirements, and much, much more. Failing to comply can be costly: The EU AI Act imposes penalties of up to €35 million or 7% of global annual turnover for violations, and other laws and regulations may soon be just as costly.
The list of risks associated with vendor lock-in is much longer than the few listed above, but the good news is that there are strategies your organization can use to avoid or mitigate AI vendor lock-in.
Simply put, don’t put all your eggs in one AI basket. Using modular architecture and keeping your data, orchestration, models, and application layers separate can, when possible, mitigate the risks associated with vendor lock-in. Using microservices and platforms like Docker to enable container operations can make it easier to switch AI vendors without labor-intensive updates or rebuilds.
Consider placing an abstraction layer, like AutoGen or LangChain, or others, between the LLM vendor and your actual product. Using a middleware wrapper, rather than placing API calls to your AI of choice directly from your app, can reduce single-vendor dependency and make switching AI vendors easier down the line.
Making open data formats like JSON or Parquet and open API conventions standard across the enterprise can ensure that elements like prompts, embeddings, and logs can move between providers. Many open standards are also customizable, enabling your team to configure them to meet your company’s unique needs.
Open-weights models, like Llama, deliver access to trained model parameters (weights), but keep their training code and architecture private. Open-source models, on the other hand, offer access to both weights and source code. Both models provide additional control and configurability, though open-source models offer greater customization.
Both can keep costs down, limit or even eliminate contract lock-in, and provide enhanced protection for sensitive data. But, as usual, these come with a caveat: Running open-source or open-weights models locally requires expertise some companies may not have in house.
The most non-technical item on this list may be the most impactful, but it also requires technical leaders to do something they’d rather avoid: Slogging through contract legalese.
That may not sound like a good use of tech experts’ time, but a 2025 study by Stanford Law School found that: “92% of AI vendors claim broad data usage rights, only 17% commit to full regulatory compliance, and just 33% provide indemnification for third-party IP claims.” Those numbers are well outside the kind of norms companies would expect from other SaaS vendors, so it’s while the procurement and legal teams redline items related to SLAs, payment terms, and indemnification, tech leaders should consider adding their own redlines:
Other contract stipulations often include clauses governing code/model escrow, self-hosting rights, intellectual property rights, and more.
Vendor lock-in risks have been around since long before the rise of AI. But while being locked into an AI vendor introduces many of the same risks as, say, a cloud provider, it also creates new, bigger — and in some cases, even existential — risks.
To avoid AI vendor lock-in, tech leaders should:
Each of these tactics can help reduce single-vendor dependencies, but together, they can form a smart strategy that ensures optionality, maximizes flexibility, and protects users, data, and the organization itself.
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