LLM adoption is exploding. Costs are too.  LLMs scale faster than traditional FinOps can manage. Token-driven billing, provisioned throughput, and opaque usage make spend unpredictable and often unsustainable. Enterprises adopting AI risk margins eroding before revenue catches up.

This whitepaper shows you how to get the economics of AI under control using Cloud Efficiency Posture Management (CEPM).

What You’ll Learn

LLM costs are already one of the fastest-growing categories of cloud spend. Traditional FinOps practices weren’t built for this volatility. CEPM provides the visibility, alignment, and proactive optimization needed to keep AI workloads efficient and profitable.

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We’ll go beyond theory. This whitepaper gives you a framework to connect technical decisions to financial outcomes.

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We’ll dive into the following topics:

• Token economics and caching strategies: how prompt design changes unit costs across AWS, Azure, and GCP
• Deployment locality tradeoffs: balancing compliance requirements against hidden cost premiums
• Throughput planning: right-sizing provisioned units to avoid wasted capacity and surprise overages
• Visibility gaps in billing: why native tools fall short, and how to build a virtual cost layer
• Outcome-based FinOps: moving past token counts to metrics like cost per resolved ticket or generated draft

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If your enterprise is building with LLMs, this paper is essential.

It shows you how CEPM transforms AI economics from a source of risk into a source of advantage.

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