Executive Summary
Kubernetes is now the backbone of modern cloud and AI infrastructure as it powers everything from containerized microservices to GPU-heavy LLM training and inference. But the same elasticity that makes Kubernetes so attractive can also cause cloud bills to spiral out of control. With the Kubernetes-for-AI market projected to grow from $2.1B in 2024 to $21.6B by 2033, engineering leaders, FinOps teams, and CFOs are under pressure to prove that every CPU core and GPU hour is earning its keep.
What You Get in this eBook?
This eBook connects real-world Kubernetes patterns with practical frameworks, metrics, and runbooks so you can:
- Understand the full cost of Kubernetes clusters.
- Track the right cost and performance metrics.
- Apply FinOps to Kubernetes and AI workloads.
- Implement best-practice levers for cost savings.
- Use AIOps to move from reactive to proactive operations.
- Evaluate managed Kubernetes-as-a-Service (KaaS) options.
Who Should Read This eBook?
This eBook is written for leaders and practitioners responsible for both Kubernetes performance and cloud economics, including:
- CIOs, CTOs, and Heads of Platform / Infrastructure
- VPs of Engineering, DevOps, and SRE / Platform Teams
- FinOps Leaders, Cloud Cost Managers, CFOs, and Finance Business Partners
- AI/ML Leaders, Data Science Managers, and Product Owners building on Kubernetes
- Cloud Architects, Infrastructure Managers, and Operations Teams running multi-tenant or AI-heavy clusters
If you’re accountable for scaling Kubernetes and AI workloads without scaling your cloud bill, this eBook will help you turn Kubernetes from a cost center into a measurable, high-ROI growth engine.
