Companies that rely on cloud computing are finding it increasingly difficult to manage the costs of their infrastructure, a concern that compromises their effective operation and planning for the future. In an economic environment where efficiency is key, gaining clear and real-time visibility of cloud-related expenses and usage patterns becomes critical. This insight allows not only for more agile decision-making, but also for maximizing the value of investments made in the cloud, ensuring a more sustainable and efficient organizational growth.
In this context, the dynamic nature of cloud usage emphasizes the importance of continuous and rigorous cost monitoring. Companies need to ensure that their expenses do not exceed expectations and that they only pay for the resources they actually need. It is essential to be able to measure the value that the cloud brings, which is done by quantifying the corresponding costs.
For organizations managing environments with multiple accounts in services like AWS, tracking these costs can be done at the account level. However, to allocate expenses to specific cloud resources, it is crucial to implement an effective tagging strategy. The combination of well-managed accounts and strategic tagging provides the best results. Establishing an effective cost allocation strategy from the start is vital for managing expenses properly and facilitating future optimizations that can reduce overall spending.
This article highlights the importance of broad and effective tagging governance across multiple accounts. AWS offers tools and services that enable deep visibility and comprehensive control over costs. Implementing automated policies for compliance verification translates to significant optimization in machine learning (ML) environments.
To effectively manage resources through a tagging strategy from the beginning, it is essential to identify the appropriate tags that gather all relevant information. Common categories for tag design include cost allocation, automation areas, access control, technical information, and regulatory compliance, among others.
Adopting a consistent and programmatic approach to tagging across the infrastructure is crucial. It is important to define which resources need tagging and establish mechanisms to apply mandatory tags without including personal data, as these tags are visible and not encrypted.
Additionally, in AWS, costs associated with running ML workloads mainly come from computing resources such as Amazon EC2 instances and storage in Amazon S3 used for datasets and models. Implementing a tagging system in services like Amazon SageMaker, Amazon DataZone, and AWS Lake Formation is essential to accurately track these expenses.
Finally, monitoring through tools like AWS Cost Explorer, along with cost reports and the use of additional services, allows companies to analyze and visualize costs effectively. This enables them to receive alerts when approaching budget limits and ensures that cloud operations are in perfect alignment with business needs, optimizing their efficiency and strategic use.
via: MiMub in Spanish
