I'm saying this from a real-world experience we recently had - a fintech leader dramatically reduced their Kubernetes costs by implementing some of these methods.

So what are the three ways?

• Scale Predictively: Use AI to forecast infrastructure demand. Specifically, use machine learning models to predict traffic and the number of pods required to serve that traffic. Predict better to save more and conserve more.
• Automate Across Multi-Cluster: Avoid manual tracking of costs across multiple individual clusters. If your team's applications are spread across various regions and clouds, utilize a framework that consolidates the resources in these individual clusters into a single accounting framework. This allows for more efficient cost tracking and management.
• Have Meaningful Reporting and Chargeback: Visibility is power. Get reporting by team. Group the charges by namespaces owned by teams. It’s not just about compute costs; track other cost factors too, like load balancer costs and persistent volume (PV) storage costs. Make sure your cost management tool easily supports these capabilities.

By the way, how are companies in general doing on cost management, reduction, and reporting?  According to a recent Cloud Native Computing Foundation (CNCF) survey, here’s some data on K8s costs today:

• 49% report rising costs due to Kubernetes complexities.
• 40% only 'estimate' their expenses.
• 19% have accurate cost data.
• 2% have implemented a chargeback model.
• 38% have no cost monitoring.

Can you believe that? Nearly 4 out of 10 organizations are flying blind!

Back to our fintech leader - here are some metrics on their outcomes:

• A 65% reduction in CPU minutes for crucial services.
• It’s estimated to result in over $800,000 in annual savings, with a greater than 25% cost reduction.

That’s real savings – no small change, right?

How do you manage Kubernetes costs? Share your insights and strategies. And just for fun, what would Michael do?