The correct option is They are restricted to models they can deploy on their own hardware which rules out the largest API only foundation models hosted by cloud providers.

Running entirely in the company’s own data centers on hardware it owns means the platform cannot rely on cloud hosted model endpoints. That excludes major provider hosted foundation models that are only accessible through managed APIs, such as those offered through Vertex AI. The team must choose models that can be self hosted, which typically means open source or vendor models that explicitly support on premises deployment. This is the most significant model layer constraint because it narrows the feasible model set before any tooling or development approach is considered.

Total cost of ownership will drop markedly compared with a managed public cloud is not correct because committing to operate your own hardware and facilities usually increases capital costs and ongoing operations effort. Managed cloud services are designed to offload much of that burden, so this claim is not a constraint of the model layer and is unlikely to hold.

It requires using Vertex AI for all model lifecycle tasks is not correct because Vertex AI is a managed Google Cloud service and is not required when running entirely on your own infrastructure. The team could use self hosted MLOps tools and frameworks, and their residency rules would likely prevent use of managed cloud model endpoints anyway.

The team must adopt a low code or no code toolkit to build the application is not correct because development style is a choice and not a constraint imposed by the decision to run on premises. The team can build with full code frameworks, SDKs, or any mix that fits their skills and governance needs.

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