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Overview In Cloud Financial Management, targeting high-value managed services is a sign of a mature FinOps practice. While teams often focus on rightsizing common compute resources like EC2 instances, specialized services such as Amazon MQ frequently go unexamined. This oversight can be a significant source of cloud waste, as Amazon MQ instances carry a notable […]

Overview In cloud financial management, observability costs are a silent but significant driver of budget overruns. While teams focus on optimizing compute instances, the ancillary costs of monitoring these resources can grow unchecked. A primary example of this “observability tax” is the data pipeline between Amazon Relational Database Service (RDS) and Amazon CloudWatch Logs. Exporting […]

Overview In the constant pursuit of cloud efficiency, migrating workloads to more cost-effective infrastructure is a core FinOps discipline. One of the most impactful optimizations available on AWS is transitioning Amazon Aurora database clusters from traditional x86-based instances to AWS Graviton processors. This move leverages AWS’s custom-designed ARM-based silicon to deliver a powerful combination of […]

Overview In modern machine learning operations on AWS, the rapid iteration and experimentation facilitated by services like Amazon SageMaker can lead to significant, often overlooked, financial waste. While teams focus on the compute costs of training jobs and active endpoints, a silent cost driver emerges: the accumulation of idle model artifacts. An Amazon SageMaker "Model" […]

Overview As organizations embrace Generative AI, services like Amazon Bedrock have become central to innovation, allowing teams to customize powerful foundation models. However, this rapid experimentation often creates a new form of cloud waste: idle custom models. When development teams fine-tune or import models for projects, proofs-of-concept, or testing, these assets can be easily forgotten […]