In 2017, Netflix gave a talk at AWS re:Invent that left a lasting impression on me. The session, "Tooling Up for Efficiency: DIY Solutions @ Netflix", presented by Andrew Park and Sébastien Delarquier, shared how Netflix built in-house solutions to optimize cloud costs at scale.

This talk profoundly shaped my perspective on AWS cost management. Netflix laid out their approach to cloud management, with a particular focus on cost efficiency. They also made a compelling case for why large AWS users might benefit from developing their own cost optimization tools.

Trade-offs in Cloud Management
One key insight that I believe shapes any organization’s cost optimization efforts is understanding the trade-offs required when setting cloud management priorities:

• Innovation – Experimenting, iterating quickly, and developing new features.
• Reliability – Ensuring uptime, system resilience, and handling failure gracefully.
• Security – Protecting data, preventing breaches, and meeting compliance needs.
• Efficiency – Optimizing cloud costs and resource utilization.
  

No organization can maximize all four at once. If you’re going to prioritize something, something else must be deprioritized. Conversely, if everything is a priority, nothing truly is.
With this in mind, Netflix walked through how they manage cost efficiency and the tooling they developed to support it.

The Key Takeaway: Data-Driven Decision Making
Whether you’re using commercial tooling, cloud-native cost tools, DIY solutions, or a mix of all three, their approach still makes sense.
The biggest takeaway for me? Data-driven decision-making is essential for cloud cost optimization. The approach will feel familiar to many enterprise data teams—it’s a blend of business intelligence (BI), data analysis, data engineering, and data science.

A Personal Observation
Many of the capabilities Netflix outlined in their 2017 talk have since been incorporated into AWS’s native cost and billing tools, as well as the Well-Architected Framework. These tools are a great starting point—and for most shops, they get you most of the way there.

However, I’ve found that developing additional tooling for deeper data analysis, automation, and alerting can significantly enhance AWS’s built-in capabilities.

What’s Your Cloud Cost Management Strategy?
How does your organization approach AWS cost management? Do you prefer native AWS tools, commercial dashboards, DIY solutions, or a combination? Join the conversation over on LinkedIn, or connect with me directly on my LinkedIn page to share your experience or explore how we can help build a tailored cost optimization strategy for your organization.

After three years of public testing, Karpenter 1.0 was officially released in August, and since then, it has been rapidly evolving. Now at version 1.2.1, this open-source cluster autoscaler is ready for production workloads, providing a smarter way to manage Kubernetes infrastructure by dynamically selecting the most cost-efficient instance types without sacrificing performance.

​Karpenter is built for modern cloud-native environments, where flexibility is key. Whether you're scaling your infrastructure to handle peak traffic or optimizing resources during quieter periods, Karpenter helps you maximize efficiency and reduce costs.

Ready to Optimize Kubernetes at Scale? 
​Are you using Karpenter in your Kubernetes environment, or are you considering making the switch? Let’s explore how Karpenter can help optimize costs and enhance scalability for your AWS infrastructure. Join the conversation over on LinkedIn, or connect with me directly on my LinkedIn page to discuss best practices and implementation strategies for cost-effective Kubernetes management!

AWS's S3 Inventory service, when paired with Amazon Athena, can revolutionize how you manage and optimize storage lifecycle policies. By querying S3 Inventory data directly in Athena, you gain actionable insights into your storage usage, helping you make data-driven decisions to reduce costs and improve efficiency.
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How S3 Inventory and Athena Can Optimize Storage

• Analyze usage patterns: Query object attributes such as age, size, and storage class to better understand your data.
• Optimize costs: Identify objects that can be transitioned to cheaper storage tiers, including Intelligent-Tiering for automated savings.
• Streamline deletions: Locate outdated or unnecessary files that can be safely removed to free up storage.

​Whether you prefer running queries directly in the Athena console or integrating them into Python workflows, S3 Inventory is a powerful tool for managing large-scale S3 storage environments.

Want to Optimize Your S3 Storage Strategy? Are you using S3 Inventory, or are you considering implementing it? Let’s discuss how you can leverage these tools for smarter storage management and cost savings. Join the conversation over on LinkedIn, or connect with me directly on my LinkedIn page to explore tailored solutions for AWS cost optimization and FinOps best practices!

​In FinOps, leveraging Parquet’s columnar storage format with tools like Amazon S3 and Athena can significantly reduce data transfer and storage costs. These are key factors in cutting your AWS bill while also speeding up queries.

But here’s where it gets even better: combining Parquet with Python enables powerful cost analysis and automation. With libraries like Pandas and PyArrow, you can process Parquet files efficiently and build custom FinOps dashboards to track and optimize cloud spend in  real time!

Ready to Explore Parquet and Python for FinOps?
Are you already using Parquet and Python in your FinOps toolkit, or are you considering them to optimize your cloud costs? Let’s dive deeper into how these tools can transform your cost management strategy. Join the conversation over on LinkedIn, or connect with me directly on my LinkedIn page to learn more or explore how we can help your organization achieve AWS cost optimization success!

When forming a FinOps team, having the right mix of skills is crucial for effective cloud cost management. Below is the ideal blend of skills needed for a team ready to tackle the complexities of cloud optimization:
Core Team Skills

• IT Finance: A solid understanding of ROI, budgeting, and cost models is essential to bridge the gap between IT and Finance.
• Operations: Hands-on experience with managed services, automation, and recommending operational improvements is invaluable.
• Business Intelligence/Data Analysis: Analyzing business operations data to extract actionable insights is key to informed decision-making.
• Data Engineering: Managing and preparing data for efficient analysis ensures accurate and timely insights.

Pro-Tip for Getting Started
​If you’re just beginning to build your FinOps team, starting small with a versatile, cross-skilled resource can be highly effective. Look for individuals with the following key skills:

• Cloud Operations / DevOps: Expertise in cloud optimization and the ability to collaborate with Development and Engineering teams.
• Data Engineering: Data wrangling is critical, and familiarity with Python can help manage billing data, resource inventories, and price lists.
• BI/Data Analysis: Proficiency with spreadsheets and visualization tools is essential for generating insights for Finance and leadership.
• Management: As your organization grows, management experience will be crucial for scaling the team and driving long-term success.

Even with just one versatile team member, leveraging automation can significantly enhance your cloud cost optimization efforts. As the workload increases, this individual can take on a leadership role and guide the growth of the team to meet evolving demands.

Have Questions About Building a FinOps Team?
Whether you're just starting out or scaling your team, we’d love to hear from you. Join the conversation over on LinkedIn, or connect with me directly on my LinkedIn page to share your challenges, ask questions, or explore opportunities to collaborate on building the ultimate FinOps strategy. Let's work together to optimize your cloud costs!