What vendor lock-in means
Vendor lock-in means: switching to another provider has become so expensive, effortful, or risky that it's practically off the table – you're tied in. That's not inherently bad, but it should be a conscious decision, not one you slide into by accident.
Where lock-in arises with AI
With AI, dependence arises in several places: prompts optimized precisely for one particular model; a model fine-tuned for one provider (module 16); proprietary interfaces and data formats that aren't easy to take with you; and integrations built deep into workflows. The more of these that come together, the higher the switching barrier.
How to bound lock-in
Four approaches help: route the model through a swappable abstraction layer instead of wiring it in hard (then the provider can be swapped); keep your own data exportable and under your control (e.g. RAG on your own data, module 15); don't over-tune prompts to a single model; and assess switchability before you start, not only when it's urgent.
Lock-in isn't always bad
A certain amount of dependence is often a fair price for convenience and performance – a turnkey proprietary service saves effort (module 26). The point isn't to avoid every dependence, but to enter it consciously and know how expensive an exit would be.
Why this matters for you as a decision-maker
Before you commit to a provider, it's worth asking: how much effort would switching be in a year? Is our data portable? That's not paranoia, but a conscious weighing of convenience today against freedom of action tomorrow (related to build vs. buy, module 6).