Well-researched thought leadership content from our FinOps & cloud-certified experts

This blog explores why cloud optimization becomes more challenging once the obvious sources of waste have been addressed. It highlights how balancing usage optimization with automated rate optimization can help FinOps teams capture savings efficiently while reducing the burden on engineering teams.

In this blog, explore how GCP load balancing costs can be analyzed beyond billing data using resource configuration, Cloud Monitoring metrics, load balancer classification, and actionable recommendations.

In this blog, explore the fundamentals of AI tokens, understand what drives token spend, and discover practical strategies for optimizing prompts, context, models, and AI workflows.

MCP does not require an LLM. See how a Python pipeline uses MCP servers for cloud migration cost estimation, validation, caching, and reproducible results. while keeping results reproducible

In this blog, we explore how to automate AWS compliance remediation using EventBridge and Lambda, helping teams detect issues faster, apply safe fixes, and reduce repetitive manual work.

Explore FinOps for AI, from GPU utilization and LLM token costs to AI cost visibility, governance, workload optimization, and measuring AI ROI.

Explore how prompt caching on Amazon Bedrock reduces LLM input token costs, improves latency, and helps optimize repetitive workloads without changing your model.

Explore 12 cloud cost management challenges teams face in 2026, with practical solutions for rising spend, rightsizing, Kubernetes, data transfer, visibility, and optimization.

We tested every cost-saving feature in Kubernetes 1.37 hands-on — scale-to-zero, memory QoS, and more — so you know what actually cuts your cloud bill.