Cloud AI made it incredibly easy to build intelligent products.
You connect to an API, send a prompt, and within seconds, your application responds with something that feels smart. For developers, founders, and product teams, this has been transformative. What once required deep research and infrastructure can now be achieved with a few lines of code.
But ease of use often hides long term tradeoffs.
As more teams move from experimentation to production, a deeper issue is starting to surface. The intelligence powering their applications is not something they control. It is something they access.
This distinction is subtle at first, but it becomes critical over time.
Because when intelligence lives outside your system, your product becomes dependent on it.
And dependency, at scale, becomes constraint.
Understanding AI Vendor Lock In
AI vendor lock in refers to a situation where your application becomes tightly dependent on a specific AI provider, making it difficult, expensive, or risky to switch to alternatives.
This is not a new pattern.
The same thing happened with cloud infrastructure. Companies built deeply integrated systems on top of specific platforms, only to realize later that moving away required significant effort and cost.
With AI, the lock in goes deeper.
It is not just about where your application runs.
It is about how your application thinks.
When your core functionality depends on external APIs, you are effectively outsourcing decision making, generation, and reasoning to a system you do not own.
This creates a dependency that is harder to unwind than infrastructure choices.
