Tagging Your Cloud Resources: How to Improve Your Cloud Financial Management Strategy?

Looking at traditional industries, such as manufacturing, they clearly understand the costs associated with each component of their production process. Manufacturers meticulously track expenses for raw materials, labour, equipment, and other factors contributing to the overall cost of producing, marketing, and selling their goods. This detailed knowledge empowers them to optimise their processes, reduce waste, and make informed decisions about pricing and profitability.

Efficient cloud financial management

As a founder or leader of a startup, it is worth asking yourself:

• Do I have comparable insight into the costs associated with each component of my cloud-based infrastructure?
• Can I accurately track and allocate expenses for my cloud services?
• Do data drive my decision-making process?
• How do I track and measure my KPIs?

If the answer is unclear or uncertain, it is time to reevaluate your Cloud Financial Management strategy. Efficient cloud financial management has become crucial for numerous aspects, such as informed decision-making, governance, automation, and budgeting. The implementation of the Tagging Strategy plays a significant role in this.

The Tagging Strategy is built upon utilizing metadata such as Google Cloud Labels, Amazon Web Services (AWS) Tags, Careem Exchange Tags, or other cloud providers’ equivalents. These key-value pairs allow everyone from executives and financial planners to developers and product managers to categorize and organize resources, simplifying their managing, searching, filtering, and reporting.

The tagging strategy brings several benefits to the table:

1. Cost allocation enables more granular cost reporting and analysis by associating expenses with specific projects, departments, or teams. This insight allows companies to make informed decisions about product pricing and measure the ROI of their initiatives.
2. Resource organization becomes more manageable as tags and labels help categorize resources by purpose, owner, environment, or other relevant criteria, promoting accountability within the organization.
3. Access control where tags and labels can be used to implement access policies that ensure only authorized users can manage specific resources.
4. Even automation is facilitated by the tagging strategy as it provides triggers for scripts and other automated processes, such as terminating instances or monitoring machines with specific tags.

Let’s take the below example

A group of resources that are tagged with the following values:

environment: research / product: product_a / team: team_c / function: ai_model_a.

According to these tag values, we could answer questions like: How much does team_c cost to train ai_model_a related to product_a within the research environment?

Questions like this could help us evaluate future development, understand the potential ROI, or even identify inefficiencies related to executing the ai_model_a in that specific case.

In other cases, we could create a governance policy in which each resource tagged with the above values is automatically terminated at night and on weekends. We all know how expensive GPUs can be, so proactively preventing inefficiency could be very beneficial.

To maintain order and efficiency, companies adhere to several best practices:

• Consistency: They establish a consistent tagging and labelling scheme across all resources to simplify management and reduce confusion.
• Documentation: They document the tagging and labelling strategy, including the key-value pairs used and their purpose, to ensure adherence across the organization.
• Enforcement: They implement mechanisms to enforce the tagging and labelling strategy, such as using automated tools to apply tags and labels to newly created resources.
• Review: They periodically update the tagging and labelling strategy to ensure its relevance and effectiveness.

The use of tags exposes numerous challenges, out of which one prevails, and that is the formulation of the strategy itself.

Other challenges may involve shared costs, implementation, granularity levels, etc. With a team of experts from diverse disciplines, Vortex can effectively identify your requirements and assist you in deriving greater value from your data.