FM Deployment with SageMaker AI
Understand how to deploy foundation models for generative AI using Amazon SageMaker AI. Learn to choose and configure the right endpoint types—real-time, asynchronous, or serverless—to balance latency, cost, and workload demands in production scenarios. Gain insight into managing large models with predictable performance and scalable inference.
SageMaker AI is typically introduced when generative AI workloads require more control than fully managed or on-demand inference options can provide. This often happens when models are large, customized, or expected to serve production traffic with predictable performance characteristics. In these scenarios, inference behavior depends on both the model’s capabilities and how infrastructure, memory, and execution time are managed.
Generative AI systems tend to surface these needs quickly. Large language models have long initialization times, high memory footprints, and token-based execution patterns that make cold starts and opaque scaling behavior unacceptable. When an application requires consistent latency, sustained throughput, or the ability to process very large requests, SageMaker AI becomes the natural choice for hosting inference.