Category: Amazon Machine Learning

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.

As we continue our five-part series on optimizing costs for generative AI workloads on AWS, our third blog shifts our focus to Amazon Bedrock. In our previous posts, we explored general Cloud Financial Management principles on generative AI adoption and strategies for custom model development using Amazon EC2 and Amazon SageMaker AI. Today, we’ll guide you through cost optimization techniques for Amazon Bedrock, AWS’s fully managed service that provides access to leading foundation models. We’ll explore making informed decisions about pricing options, model selection, knowledge base optimization, prompt caching, and automated reasoning. Whether you’re just starting with foundation models or looking to optimize your existing Amazon Bedrock implementation, these techniques will help you balance capability and cost while leveraging the convenience of managed AI models.

If you or your organizations are in the midst of exploring generative AI technologies, it’s important for you to be aware of the investment that comes with these advanced applications. While you are aiming at the expected return on your generative AI investment, such as, operational efficiency, increased productivity, or improved customer satisfaction, you should also have a good understanding of levers you can use to drive cost savings and enhanced efficiency. To guide you through this exciting journey, we will publish a series of blog posts filled with practical tips to help AI practitioners and FinOps leaders understand how to optimize the costs associated with your generative AI adoption with AWS.

With re:Invent 2024 in the books, and over 50 launch announcements, here are four that we’re most excited about. The overarching theme of these launches appears to be leveraging Amazon’s automation capabilities to optimize costs and improve efficiency for customers.

We’re excited to announce the release of AWS Cloud Financial Management digital training courses. These are four 1-hour courses that will get you familiarized with key AWS solutions to solve your daily FinOps needs, and equip you with cost optimization techniques for commonly used AWS services.

Get near real-time visibility into anomalous spend by receiving AWS Cost Anomaly Detection alert notifications in Slack using AWS Chatbot. With faster visibility and insights you can reduce cost surprises, enhance control, and proactively increase savings. AWS Cost Anomaly Detection uses advanced Machine Learning to help identify and evaluate the root cause of spend anomalies. […]

  AWS helps customers drive business value and optimize cloud spend. Yet, understanding these drivers and how to maximize the benefits of AWS requires education and practices. AWS Cloud Economics helps customers build business cases beyond pure cost savings and helps customers on the platform optimize financial practices & costs. AWS Cloud Economics team works […]