How to Evaluate an AI Vendor Before You Depend On It
A buyer's checklist for AI model and infrastructure vendors: lock-in, data handling, exit cost, version stability, and what happens when their terms change.
Picking an AI vendor is not like buying a tool. You are deciding who gets to change the rules on a thing your product depends on. The model behind a vendor's API can shift behavior overnight. The pricing can double. The terms can quietly add a clause about training on your data. None of that shows up in the demo.
So the question to ask is not "is this impressive" but "what happens to me when this vendor changes". Capability is everywhere now. The thing worth evaluating is how much control you keep, and how cheaply you can walk away. Here is the checklist I run before I let anything become load-bearing.
Lock-in and exit cost
Start at the end. If you had to leave this vendor in ninety days, what would it cost you? Not the subscription. The rebuild. Map every place their specific format, prompt structure, fine-tune, or proprietary feature is wired into your product. The deeper that goes, the more leverage they have over your pricing forever.
The cheapest insurance is an abstraction layer between your product and any single model. If swapping a provider means changing one config file instead of rewriting a feature, you have kept your exit cheap. A vendor who makes that hard is telling you something about how they plan to treat you later.
Data handling and where it lives
Ask three plain questions and get them in writing. Do they train on your inputs or outputs. Where is your data physically stored and processed. What happens to it when you cancel. A confident sales answer is not the same as a contract clause, so push until it is in the agreement.
This is the part most teams skim and later regret. If your customers' data flows through a vendor with vague retention terms, you have inherited their risk and put your own name on it. Owning the things your business depends on starts with knowing exactly where the sensitive parts go.
Version stability
Managed model endpoints change underneath you. A provider deprecates a version, or silently updates it, and the prompt that worked last month now returns something subtly wrong. Ask whether you can pin a specific version, how much notice you get before one is retired, and how long old versions stay available.
If the answer is that the model can change without warning, your product is non-deterministic in a way you do not control. For anything that has to be reliable, that is a real problem, not a footnote. Build your evals so you can detect a behavior shift the day it happens instead of finding out from a customer.
Pricing and terms under you
The vendor you sign is not the vendor you will have in two years. Read for the clauses that let them change pricing, rate limits, and usage terms unilaterally. Look at their history. Have they raised prices abruptly, killed a tier, or changed who is allowed to use the product. Past behavior is the best signal you will get.
Then do the math on the moment it stops being cheap. Managed services are a good deal at small scale and quietly become a tax as you grow. Know the volume where renting turns into a liability you would rather own, so the decision to move is yours and not forced on you in a panic.
The rule underneath the checklist
Every line here points the same direction. Keep your exit cheap, know where your data lives, control your versions, and read the terms for who holds the power. A good vendor relationship is one you could leave and choose not to.
This is the same logic behind how I run my own portfolio: own the things your business depends on, and rent only the things you could replace by Friday. Evaluate vendors as if you will one day need to fire them, because eventually you will.