Guide Compares Five Options for Deploying AI Model Inference

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A deployment guide outlines five ways to serve AI models: managed model APIs, managed ML endpoints, serverless GPU services, self-hosted serving on containers or Kubernetes, and platform layers that run containers across clouds. It recommends choosing among them based on factors such as whether the model is proprietary or open-weight, traffic patterns, latency needs, required features, data residency, cost structure, and team skills. Managed options are faster to start with, while self-hosted serving offers greater control over cost, data location, and portability. The article also highlights common inference challenges, including GPU underutilization, cold starts, autoscaling, rate limits, and multi-region availability. The guide is published by Control Plane, which is also promoted as one of the platform options discussed.

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