Optimizing LLM inference on Amazon SageMaker AI with BentoML’s LLM- Optimizer

The rise of powerful large language models (LLMs) that can be consumed via API calls has made it remarkably straightforward to integrate artificial intelligence (AI) capabilities into applications. Yet despite this convenience, a significant number of enterprises are choosing to self-host their own models—accepting the complexity of infrastructure management, the cost of GPUs in the serving stack, and the challenge of keeping models updated. The decision to self-host often comes down to two critical factors that APIs cannot address. First, there is data sovereignty: the need to make sure that sensitive information does not leave the infrastructure, whether due to regulatory requirements, competitive concerns, or contractual obligations with customers. Second, there is model customization: the ability to fine tune models on proprietary data sets for industry-specific terminology and workflows or create specialized capabilities that general-purpose APIs cannot offer.

Amazon SageMaker AI addresses the infrastructure complexity of self-hosting by abstracting ...