Author: James Yang

In 12 weeks, one Software as a Service (SaaS) organization cut AWS costs by 39% while preparing for 10x growth. This post walks through the phased approach the team used to sequence multiple optimizations without disrupting production. Each phase built on the last, compounding into a total 39% reduction within 12 weeks. You can apply […]

Navigating GPU resource constraints requires a multi-faceted approach spanning procurement strategies, leveraging AWS AI accelerators, exploring alternative compute options, utilizing managed services like SageMaker, and implementing best practices for GPU sharing, containerization, monitoring, and cost governance. By adopting these techniques holistically, organizations can efficiently and cost-effectively execute AI, ML, and GenAI workloads on AWS, even amidst GPU scarcity. Importantly, these optimization strategies will remain valuable long after GPU supply chains recover, as they establish foundational practices for sustainable AI infrastructure that maximizes performance while controlling costs—an enduring priority for organizations scaling their AI initiatives into the future.