Pick a model. Don't bet the system on it.
Models get delayed, cancelled, repriced, and retired on schedules no customer controls. Build so the model is a setting you can change, and a model release becomes news instead of a rebuild.
Model release risk is delivery risk.
If one model is the foundation, every release decision a provider makes lands on your roadmap. That is not anyone behaving badly. It is how a fast market works, and it happens across every provider.
The release you planned around slips.
A capability you built your roadmap on ships late, or does not ship at all.
The model reaches end of life.
A model you depend on is deprecated on a schedule you do not set.
The terms move under you.
Prices, rate limits, and context limits change, and your costs change with them.
Same name, different behavior.
An update shifts how the model responds, and your outputs shift with it.
The model is a part, not the foundation.
We pick the model that fits the job today and build everything around it so it can change tomorrow. The agentic platform underneath stays put.
The model is a setting.
Moving to a new model means choosing a replacement and checking it against your real work, not starting over.
Know what a swap does before it ships.
Evals score a candidate model on your real work, so you see the difference in a report, not in front of a customer.
Context lives in a layer you own.
What your agents know sits in your universal knowledge layer, so it carries over to whatever model comes next.
Agents draft. People decide.
Consequential work waits for a person, whichever model drafted it.
Model accounts stay in your name.
The spend is yours and visible, and so is the choice of provider.
Owned by you, changeable by you.
Co-built with your team and left in your hands, ready to change when a better model or tool arrives.
One question worth asking.
What happens to your AI work if your main model is cancelled, retired, or repriced next quarter? If the answer is a rebuild, the foundation is the problem, not the model.
What does model-agnostic AI mean?
Model-agnostic AI means the model is a part you can change, not the foundation the system is built on. The workflows, the evals, and the company knowledge your agents use stay the same when the model changes, so moving to a new model does not mean a rebuild.
What happens to my AI system if the model it runs on is retired or cancelled?
If the system is built around one model, a retirement or a cancelled release means a rebuild on someone else's schedule. If the model is a setting, you choose a replacement, run the evals on your real work to see what changes, and switch once it clears the bar. Foundry Solutions builds this way, so a model release is news, not a rebuild.
More answers in our FAQ.
Build on a foundation that outlasts the model.
Tell us what you run today and which model it depends on. We will tell you what a swap would take and where we would start.