A company built core operations around an AI vendor’s platform over three years.
The vendor’s pricing started reasonably. As the company became dependent, the vendor raised prices 400% at renewal. Legal reviewed options to switch providers and found none existed.
The AI had been trained on years of proprietary company data that the vendor wouldn’t transfer. Internal workflows were built specifically around the vendor’s API. Staff only knew how to operate within that ecosystem. Switching meant rebuilding everything from scratch.
The company paid the increase. They had no alternative.
How Lock-In Happens
AI vendor lock-in develops gradually, not through any single decision.
Companies start with a vendor offering attractive pricing and features. Over time, more processes get built around that specific AI system. Custom integrations multiply. Staff develop expertise in the vendor’s specific tools rather than transferable skills. Historical data accumulates within the vendor’s platform.
Each individual decision seems reasonable. Collectively, they create total dependency. By the time companies recognize the lock-in, switching costs have become prohibitive.
The Data Portability Problem
Most AI vendor contracts don’t guarantee data portability in any meaningful way.
Companies assume they can export their data and switch providers if needed. The reality is more complicated. Training data accumulated over the years often can’t transfer to a new system in a usable form. Model behavior tuned to your specific use case doesn’t transfer – a new vendor’s AI starts from scratch, even with the same underlying data.
Some contracts explicitly restrict data export or charge substantial fees for data extraction. Others allow technical export, but the format makes the data essentially unusable elsewhere without significant rework.
The Negotiation Leverage Problem
Vendors understand lock-in dynamics and price accordingly at renewal.
Initial contracts often include below-market pricing to win business and establish dependency. Once switching costs are high enough, renewal pricing reflects the vendor’s leverage rather than competitive market rates.
Companies negotiating renewals discover they have minimal leverage. The vendor knows switching costs exceed price increases up to a certain threshold. Pricing gets set just below that threshold repeatedly across renewal cycles.
What Reduces Lock-In Risk
Companies avoiding severe lock-in build portability considerations into AI vendor relationships from the start.
Contracts should specify data export rights in usable formats, not just technical access. Negotiating these terms during initial procurement, when you have leverage, works far better than trying to negotiate them at renewal when you don’t.
Avoiding excessive customization to vendor-specific features preserves switching ability. Standard implementations using common formats and APIs create more options than deeply customized integrations, only that vendor’s system supports.
Maintaining institutional knowledge about how systems work beyond just the vendor’s specific interface helps. Staff who understand underlying AI concepts can adapt to different vendors. Staff who only know one vendor’s specific tools become a switching barrier themselves.
Periodically evaluating alternative vendors, even when not actively switching, maintains market awareness and negotiating position. Vendors price differently when they believe customers have genuine alternatives versus when they know switching is effectively impossible.
The companies with the most leverage at renewal are the ones that built exit options into their original vendor relationship, not the ones discovering lock-in only when the price increase arrives.
Brian Alexander
Founding Partner, VisionAI+ Consulting Group
Former General Counsel, AI Engineering Firm
Certified ISO 42001 Lead Auditor