Category: Amazon Elastic Container Registry

The decision between Containers and Serverless Functions – or the implementation of both – should be driven by a thorough understanding of workload characteristics, cost implications, and operational requirements. As FinOps professionals, you should work closely with development and operations teams to analyze usage patterns, model costs under different scenarios, and consider factors like development velocity, operational overhead, and long-term maintainability. By leveraging the strengths of both Containers and Serverless technologies, you can build flexible, cost-effective cloud architectures that adapt to changing business needs while optimizing resource utilization and expenditure.

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.