Most enterprise AI procurement processes are built around a single question: can this vendor do what we need today. Teams run bake offs, score accuracy and latency, negotiate a contract, and move on. What almost never appears in the evaluation is a second, equally important question: what does it cost us to leave this vendor in two years. That question matters more with AI platforms than it ever did with traditional software, because the switching costs compound in ways procurement teams are not used to pricing.
With a conventional SaaS tool, lock-in is mostly a data export problem. With an AI platform, lock-in is layered across the fine tuning data you have accumulated, the prompt and evaluation harness you have built around a specific model’s quirks, the embeddings sitting in a proprietary vector store, and the operational muscle memory your engineers have developed for one vendor’s failure modes. None of that transfers cleanly. A model swap is rarely a model swap; it is a re-validation of every downstream workflow that trusted the old model’s behavior, and that revalidation work is where the real cost lives.
The organizations getting burned right now are the ones who treated the initial vendor selection as the only decision that mattered. They optimized for the lowest cost per token or the highest benchmark score, signed a multi-year agreement, and built a year of integration work on top of it. When the vendor changed pricing, deprecated a model, or simply fell behind on capability, the true cost of switching became visible for the first time, usually during a renewal negotiation where the vendor already knows leverage has shifted in their favor.
The fix is not to avoid commitment to a vendor. Depth of integration is often exactly what makes an AI platform valuable. The fix is to price the exit cost at the same time you price the entry cost, and to negotiate contract terms that keep switching a viable option rather than a theoretical one. That means data portability clauses that cover fine tuning artifacts and not just raw logs, architecture that keeps prompts, evaluation suites, and orchestration logic in your own source control rather than a vendor’s proprietary workbench, and an abstraction layer between your application code and any single model provider so a swap touches one integration point instead of every service that calls the model.
This is also, quietly, an M&A diligence issue. Acquirers evaluating a software business increasingly ask what happens to the target’s AI capabilities if a key vendor changes terms, and a target that cannot answer with a credible switching plan takes a valuation hit for the same reason a company with unassignable customer contracts does. Exit cost is not a hypothetical for the engineering team to worry about later. It is a number a buyer, a board, or a CFO will eventually ask for, and the enterprises that already have the answer are the ones who priced it in from the start.
