The Hidden Cost of Tech Debt
In the fast-evolving landscape of cloud computing, staying current is more than just good practice—it’s a financial imperative. Running outdated versions of Amazon Elastic Kubernetes Service (EKS) and Relational Database Service (RDS) can lead to unexpected and substantial costs in the form of extended support fees. These fees kick in once a version reaches its end of support (EoS) and, in some cases, can even exceed the cost of the underlying resources.

This "high interest rate" on tech debt can be a significant drain on your budget, particularly if it goes unnoticed. Let’s explore how to avoid these unnecessary expenses.

Steps to Mitigate Extended Support Costs

1. Track EoS Dates for Your Service Maintain a clear and up-to-date inventory of all your AWS services, including their lifecycle timelines. Use AWS tools or third-party solutions to monitor upcoming EoS milestones.
2. Plan Proactive Upgrades Treat upgrades as a part of your regular operational rhythm rather than last-minute emergencies. Schedule upgrade windows to minimize disruptions to your workloads.
3. Automate Upgrades Where Possible Leverage automation tools to streamline upgrades for EKS clusters and RDS instances. Reduce downtime and the risk of manual errors with scripted or managed upgrade processes.
4. Involve Your FinOps Team Collaborate with your FinOps (Cloud Financial Management) team to factor support costs into your budget planning. Ensure cost-effectiveness by weighing the benefits of extended support versus proactive updates.
   

Why This Matters
Extended support costs can accumulate rapidly, turning what might seem like a small oversight into a significant budgetary issue. By proactively managing EKS and RDS lifecycle events, you not only save money but also maintain the security and performance of your infrastructure.

Let’s Collaborate
Have questions or insights about managing extended support costs?  Join the conversation over on LinkedIn, or connect with me directly on my LinkedIn page if you’d like to explore how we can work together on optimizing your AWS environment.

AWS Compute Optimizer was launched in 2019. It's improved quite a bit since, but has has finally expanded to support RDS rightsizing! 🎉 This feature is a huge step forward for FinOps teams looking to optimize large RDS database fleets efficiently.

For those of us focused on AWS cost optimization, Compute Optimizer has been an invaluable tool—but RDS has been a tricky area to optimize efficiently, without writing a lot of code or requiring heavy manual review. 

Now, with tailored recommendations for RDS, we can streamline right-sizing decisions, reduce idle resources, and better match instance configurations to workload demands. It's truly a game-changer for those managing RDS at scale!

This update empowers us to:
• Identify underutilized or oversized RDS instances faster
• Automate rightsizing review and cut down on resource waste
• Align RDS costs even more closely with actual usage and demand
• Leverage Compute Optimizer APIs to build upon the great work done by the service team to increase lookback periods to arbitrary lengths
 
This has been a long-awaited feature for FinOps practitioners, and we're thrilled to see AWS continuing to invest in FinOps tooling to make cost management easier. Kudos to AWS for delivering this much-needed support for RDS!

Let’s Collaborate
Have thoughts or questions regarding Using AWS Compute Optimizer? Join the conversation over on LinkedIn, or connect with me directly on my LinkedIn page if you’d like to explore how we can work together on optimizing your AWS environment.

Managing AWS costs effectively doesn’t have to be complicated—especially with Python. Whether you're overseeing a few services or an entire cloud infrastructure, leveraging the right tools can unlock significant cost savings.

Why Python?
Python offers a versatile range of data management and analysis tools that are perfect for tackling AWS cost, pricing, and inventory data. Some of the most impactful tools include:

• Pandas: For powerful data manipulation and analysis.
• PySpark: To handle large-scale data processing.
• PyArrow: For efficient in-memory data representation.
• Athena/SQL: To query your data directly from S3 without managing infrastructure.

These tools are widely used in data analysis and engineering, making them an excellent fit for optimizing cloud costs.

Where Do These Tools Fit?
Whether you're working with:

• Jupyter Notebooks: Ideal for exploratory data analysis.
• Data Pipelines: Crucial for automating cost optimization processes at scale.

These tools provide invaluable insights into your cloud spend, helping you identify cost-saving opportunities and optimize resource usage.

Proven Expertise
At Brandorr Group, we’ve been managing AWS infrastructures since 2008. Over the years, we’ve found that combining Python’s powerful ecosystem with AWS Cost and Billing services is a game-changer. It has enabled our clients to:

• Reduce cloud costs.
• Streamline and optimize their infrastructures.

These efforts not only improve financial efficiency but also drive operational excellence.

Let’s Collaborate
Have thoughts or questions regarding Using Python for AWS Cost Optimization? Join the conversation over on LinkedIn, or connect with me directly on my LinkedIn page if you’d like to explore how we can work together on optimizing your AWS environment.

In case you missed it, Amazon recently partnered with Stackwatch to provide KubeCost at no cost to all EKS users.

Kubecost is the leading open-source FinOps tool to provide cost allocation, optimization, and monitoring for Kubernetes clusters. 

This partnership is a helpful addition for EKS users, especially for those managing Kubernetes workloads on AWS. KubeCost offers tools to help users track and allocate spending across clusters, namespaces, and workloads, offering insights that are often challenging to obtain with native tools alone.

By integrating KubeCost, teams can more easily break down their cloud costs and understand which resources are driving their expenses. This can be particularly valuable for teams managing large, dynamic environments where it’s essential to stay within budget while maintaining performance. The availability of KubeCost at no cost  lowers barriers to entry for EKS users aiming to make data-driven cost decisions about their Kubernetes deployments.

Full announcement and deployment instructions here.

Have questions or thoughts on S3 metrics or AWS cost optimization? Join the conversation over on LinkedIn, or connect with me directly on my LinkedIn page if you’d like to explore how we can work together on optimizing your AWS environment.