A multi-tenant software as a service (SaaS) customer support platform serves thousands of enterprise clients.

B is correct because SageMaker AI multi-model endpoints are designed for hosting a large number of models that use the same ML framework behind shared serving infrastructure. This is exactly the pattern for a SaaS platform with thousands of tenant-specific models. A dedicated endpoint per model would create excessive cost and management overhead. Serverless endpoints reduce instance management but do not solve the cost problem of managing thousands of separate model deployments. Asynchronous inference and batch transform are not appropriate because the requirement includes sub-200 ms latency, which requires real-time inference. AWS documentation states that multi-model endpoints are ideal for hosting many models using the same framework and can provide higher cost savings by sharing resources across models.