AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
In plain terms
Closed models, like GPT and Claude, are rented from a provider through an API, while open models can be downloaded and run on infrastructure you control. The choice trades convenience against control. Closed models are faster to start; open models can be cheaper at scale and keep data in-house, at the cost of running them yourself.
Closed models are professional kitchen appliances you rent by the hour. Open models are the same-grade equipment you buy, install, and maintain yourself. Renting is easier; owning is cheaper at high volume but requires your own expertise.
A healthcare company needs AI that never sends patient data outside their private cloud. They deploy an open-weight model on-premise, accepting higher infrastructure cost in exchange for full data control.
Closed models are right for most teams starting out. Open models become compelling when data residency is a hard requirement, when volume makes per-token pricing uneconomical, or when you need to modify the model itself.
Do not choose open models to save money early. The infrastructure, operations, and expertise costs easily exceed closed-model pricing until you reach significant and stable volume.
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