Deployment & economics
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.
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.
A closed model is a service you call. The vendor runs it, updates it, and you pay per use. An open model is one you can download and run on machines you control. Closed is usually faster to start. Open is what you look at when the data cannot leave, or when you need to freeze the version.
Open does not mean free of cost or free of care. You still pay for machines, for people who keep them running, and for the checks. Closed does not mean unsafe. It means you are trusting the vendor's terms and their uptime. Pick from the constraint: where the data may live, and whether you can accept a model you do not control.
Think of it this way: 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 the company. They download a model and run it on their own servers, paying more to operate it in exchange for keeping the data in-house.
A hospital cannot send clinical notes to a vendor. They download a model and run it inside their own network, so the notes never leave. A retailer with no such limit calls a closed model the same week and ships a draft feature. Different constraints, different choices, both reasonable.
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.
RaftLabs prices the running cost before the build, so a feature people like does not become a loss. You get a number for a busy month, not only a demo. The related work on our side is AI consulting.
This sits with the other deployment & economics terms on the glossary. What you pay to run AI, and the choices that change the bill. Worth reading next: API, Inference Cost, and Latency.
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