AI adoption grows fast. Azure OpenAI spend grows alongside it.

Budgets for AI climb more than 30 percent each year. Managed LLMs add value, but they also add cost pressure as usage increases. Many teams are starting to see Azure OpenAI as their fastest growing cloud expense. But Azure’s billing data doesn’t reveal where it’s allocating AI spend.

The whitepaper “FinOps for AI: Managing LLM Costs in Azure OpenAI” explains why these workloads scale in ways that traditional forecasts cannot capture. It outlines the pricing structure, visibility gaps, and workload patterns that push spend higher.

‍Real-World Considerations and Architecture Tradeoffs

The paper covers the main factors that shape Azure OpenAI economics:

• How input, output, and cached tokens define request cost.
• How deployment locality affects performance and compliance.
• How Provisioned Throughput adds predictable capacity with higher commitments.
• How model choices and version updates change cost behavior.
• How use case patterns dominate overall spend.

It also shows how CEPM tools combine configuration, usage, and throughput data to expose the real cost drivers.

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‍Azure OpenAI Lacks the Visibility FinOps Needs

Azure reports spend at the account level while applications use specific deployments. This disconnect hides the link between behavior and cost. Even strong deployment hygiene cannot create the visibility needed for accurate attribution.

Token driven workloads also change faster than compute or storage. Prompt patterns, context length, and traffic shifts alter cost in real time. Traditional FinOps cannot track this level of variability.

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‍Provisioned Capacity Creates Both Stability and Waste

Production teams adopt Provisioned Throughput for consistent performance. PTUs provide guaranteed capacity but introduce fixed cost. Many deployments run below allocation, which creates waste. Burst traffic can trigger on-demand charges that increase spend.

The whitepaper shows how CEPM compares real usage to PTU levels to right size capacity and reduce unnecessary cost.

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‍Billing Complexity Blocks Optimization

Azure OpenAI pricing mixes token volume, throughput settings, locality rules, and model behavior. This blend hides unit economics and makes it difficult to measure cost per request or cost per outcome.

The whitepaper explains how a virtual cost layer restores clarity by linking configuration data, usage metrics, and billing exports into a complete economic view.

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‍Finding What Works

The whitepaper shows how CEPM breaks large blended bills into measurable workloads. It gives teams the visibility to modernize models, right size throughput, and link technical design to financial impact.

PointFive pioneered CEPM. The platform helps teams understand LLM economics and scale AI features with confidence.