Practical FinOps (MEAP V01)

In a cloud-first world, the operating costs of your software systems are a first-order concern. Unexpected usage spikes, operational inefficiencies, and redundant services can lead to expensive bills from your cloud provider-especially as your software scales to allow for business growth. FinOps is a cloud management approach that elevates cloud cost management to the same level as other operating concerns like security, performance, and stability. Practical FinOps teaches you day to day FinOps skills, from improving visibility and assigning ownership, to optimizing compute, storage, and network usage across all major cloud networks.
In Practical FinOps: Managing cloud cost, visibility, and accountability you'll learn how to:
Build full visibility into cloud costs
Fix and enforce tagging strategies
Query and analyze cost data using open-source tools
Optimize compute, storage, network, and Kubernetes usage
Control the cost of AI/ML workloads
Implement FinOps at scale
Manage multi-cloud spend with confidence
Use AI and LLMs to automate cost analysis and recommendations
Practical FinOps is the key to identifying exactly where your costs are rising and managing them effectively through financial accountability, visibility, and collaboration. This book offers a comprehensive FinOps framework that helps flag the products and teams causing your spend to rise, avoid surprises from poor compute or unmonitored traffic, and establish automation to catch anomalies and enforce policies.
about the book
Practical FinOps reveals practical strategies to measure and reduce your cloud spend. It's based on pioneering techniques that author Mohamed Labouardy has developed working with engineering and finance teams across fast-scaling companies. In it, you'll find the lesser-known areas where companies bleed cloud costs, from Kubernetes and shared services to multi-cloud egress. Every technique is illustrated with real-world examples, from building a Cloud Asset Inventory that can organize and visualize your resources, to modeling the cost of an AI/ML pipeline.

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