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Integrating managed LLM services into your workflows introduces many challenges. Cost and cost management rank high among them. If managed LLM pricing feels confusing, that’s because it is. It is confusing for everyone. Managed LLM pricing models differ fundamentally from traditional cloud pricing models.
Azure OpenAI pricing does not follow a simple cause-and-effect model where usage maps cleanly to a bill. Instead, cost depends on several variables, including input tokens, output tokens, cached tokens, deployment locality, and provisioned throughput. Even when prompts look straightforward, the model determines the response. In many cases, that response represents the most expensive part of the workflow and remains only partially controllable.
These variables compound. Small engineering decisions around prompt structure, model selection, or deployment configuration can trigger large cost swings. Without a clear understanding of where costs originate or how to make adjustments, spending can escalate quickly.
The pricing model itself is not the only reason integrating managed LLM costs is so difficult.
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Cost visibility presents an even greater challenge. In Azure OpenAI, configuration and usage exist at the deployment level, while costs aggregate at the account level. A deployment represents a single model endpoint that applications call. An account acts as an administrative container that can hold multiple deployments. Although applications interact directly with deployments, Azure reports all costs at the account level. As a result, teams can see how much they spent, but not which applications or use cases actually drove the cost.
When multiple applications share a single Azure OpenAI account, teams cannot attribute costs to specific applications or use cases, even if they separate model deployments. Azure still reports all spending at the account level. This means teams cannot tell which workload is responsible for rising costs or which use cases have the greatest financial impact. Native Azure cost data remains too high-level to support unit economics. Bills look complete, but they do not explain what actually drove the spend or where teams should focus optimization efforts.
Without deployment-level cost attribution, optimization becomes impossible. Teams do not have the visibility needed to identify inefficiencies or explain cost increases when they appear on the bill. Decisions about capacity planning, model upgrades, or performance tradeoffs rely on assumptions instead of data. In practice, teams know they are overspending, but they cannot see where or why it is happening.
Where do we go from here?
A single AI-powered feature often relies on more than one model, along with supporting cloud services and integration layers. Together, these components determine the real cost of a use case. For this reason, FinOps for AI must measure unit economics using business outcomes, not raw infrastructure metrics. Measuring cost per resolved customer query or cost per generated artifact shows efficiency more clearly than token pricing alone. This approach gives finance and engineering teams a shared way to evaluate how technical decisions affect cost.
A virtual cost layer makes this outcome-based view possible. It connects usage patterns and configuration choices directly to financial impact. This includes factors such as context window size, provisioned capacity premiums, differences between pricing models, and workload behavior. With this visibility, teams can assess tradeoffs between accuracy, latency, and cost before changes reach production. Without it, those decisions happen blindly.
But how do we get there?
Cloud Efficiency Posture Management provides the framework for managing LLM economics, including use case economics. CEPM is the process for achieving ongoing efficiency through detailed, contextual analysis of AI workload environments and usage patterns. It enables engineers to remediate issues quickly by providing the context needed to validate and act on savings opportunities, including AI generated remediation prompts. CEPM platforms integrate directly into engineering environments, including MCP servers, Jira, and ServiceNow.
CEPM is effective for AI workloads because it provides a virtual cost layer. It combines financial data with technical metrics to expose efficiency at the workload level. Rather than viewing spend only at the account level, CEPM breaks costs down by individual deployments and applications. This makes unit economics visible and ties cost directly to how AI workloads operate. As a result, AI cost management shifts from reviewing bills after the fact to measuring efficiency continuously as workloads evolve. Teams can model changes, compare optimization options, and plan capacity using real data instead of assumptions.
Interested in learning more about FinOps for AI: Managing LLM Costs in Azure OpenAI? Read our whitepaper.
Ready to see PointFive’s CEPM platform in action? Book a demo.
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