Category: Artificial Intelligence

​​​FinOps practitioners now have access to a growing lineup of AI tools, but matching the right tool to each use case is the difference between accelerating your practice and adding unnecessary complexity. AWS designed each tool in the AI lineup for different context and a different type of work. ​​In this blog we are going to go […]

As you scale your use of large language models (LLMs), a type of foundation model (FM) on Amazon Bedrock, costs increases. But traditional cloud cost tools only tell you how much you spent, not why. That gap between billing data and operational insight is where cost inefficiencies go undetected. Optimizing LLM costs requires visibility into […]

Today, AWS announces the public preview of AWS FinOps Agent, an agentic AI solution that investigates cost anomalies to root cause and answers cost questions for engineers across your organization, in the tools they already use. FinOps, short for financial operations, brings finance, engineering, and business teams together to maximize the business value of cloud […]

As you scale Generative AI usage with Amazon Bedrock, a common question emerges: “Which team, application, or user is driving the Bedrock spend?” Until now, answering that question required manual reconciliation correlating AWS CloudTrail logs with billing data to map API calls back to specific identities. That approach is time-consuming, error-prone, and difficult to maintain at scale. AWS has announced AWS Identity and Access Management (IAM) Principal-Based […]

With re:Invent 2025 behind us and over 60 launch announcements, here are five hidden gems that may have gone unnoticed but still have big cost impacts. While everyone was talking about the database savings plans and AI announcements, these quieter launches are already helping customers optimize their cloud spend in creative ways.

If you are new to AWS, you may be wondering how you can learn and experiment with cloud services while keeping your spend under your control. Budget Controls for AWS is an open-source solution designed to solve this problem. This solution was designed for customers new to AWS with no prior experience. It automatically watches your spending and takes actions you define when costs reach certain thresholds. Think of it as a safety net that can send you alerts, temporarily stop resources, or even delete them to prevent runaway costs.

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.

Amazon EC2 and SageMaker AI are two of the foundational AWS services for Generative AI. Amazon EC2 provides the scalable computing power needed for training and inference, while SageMaker AI offers built-in tools for model development, deployment, and optimization. Cost optimization is crucial since Generative AI workloads require high-performance accelerators (GPU, Trainium, or Inferentia) and extensive processing, which can become expensive without efficient resource management. By leveraging the below cost optimization strategies, you can reduce costs while maintaining performance and scalability.

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.