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‍Mastering AI Cost Attribution with PointFive. In the rush to deploy Generative AI, organizations inadvertently created a new "Black Box" in their cloud bill. Whether you’re using Azure OpenAI, AWS Bedrock, or Google Vertex AI, these services often present a single, unified line item on your bill. That leaves engineering and FinOps teams guessing which specific deployments or models are driving spend.

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You cannot manage what you cannot measure. Without granular visibility, it’s impossible to accurately attribute AI usage and spend. This lack of data leads to universal failures: paying for "guaranteed capacity" that sits idle and sticking with legacy models because the cost of inertia is hidden.

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The Blind Spot: Aggregated Spend vs. Unit Economics

Most cloud billing systems were designed for an era of static infrastructure, where a Virtual Machine lived in one account and belonged to one team. Today’s AI-driven world operates on shared platforms, creating a massive visibility gap.

• The Trap of Aggregated Spend (Top-Down): Your bill says Azure OpenAI cost $50,000 last month. It doesn't tell you if that was driven by a high-value customer feature or a developer’s runaway experimental loop in a dev environment. Aggregated spend makes it impossible to separate intentional investment from waste.
• The Power of "Tokenomics" (Bottom-Up): This measures the cost per 1,000 tokens,  the cost of a single unit. Tokenomics allows you to transform "Cloud Cost" into COGS (Cost of Goods Sold), providing the clarity needed to scale.

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PointFive’s "Allocation Magic"

At PointFive, we solve this through our Data Fabric model. We don’t just analyze your bill; we ingest the telemetry of your entire AI and cloud infrastructure. By treating your telemetry data as "Data Assets", we perform Allocation Magic. Allocation Magic automatically decomposes aggregated costs into granular deployment-level insights. This turns a single, unhelpful line item into a precise map of your unit economics.

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Here is how that unique capability translates into massive savings:

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Eliminating the "Availability Premium"

In the below saving opportunity, PointFive’s engine identified a development deployment using Provisioned Throughput (PTU). While PTU is designed for mission-critical, high-traffic production latency, the data revealed an Average Utilization of only 0.6% in a non-production environment.

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By using our On-Demand Cost Simulation, the organization saw that the "Premium" price of guaranteed capacity was entirely unnecessary for a non-production environment.

• The Result: A shift to an on-demand model captured a 99% reduction in costs for that resource.
• The Proof: Our On-Demand Cost Simulation graph showed a dramatic drop in unit price the moment the fixed PTU overhead was removed.

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PointFive opportunity overview page detailing the “Provisioned Throughput OpenAI Deployment in a Non-Production Environment” savings opportunity, including engineering context and suggested remediation workflow.

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PointFive “Provisioned Throughput OpenAI Deployment in a Non-Production Environment”  opportunity analysis page detailing cost data including the “On Demand Cost Simulation” referenced above.

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Solving "Suboptimal Model Selection"

Another common cost drain is model debt. We flagged a customer’s deployment running on an older reasoning model (o1) that had been surpassed by a newer, more efficient version (o3).

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PointFive goes beyond the total bill to show Effective Cost per Request. By breaking down Input Token Cost vs. Cached Input Cost, we proved the newer model wasn't just faster, it was fundamentally more cost efficient at handling the same data.

• The Result: An 86% savings by simply staying current with the frontier of Tokenomics.
• The Proof: The visual breakdown provides the technical evidence required to justify migration by quantifying the gain in intelligence relative to the reduction in cost-per-inference. This objective data allows teams to align architectural choices with actual unit economics.

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PointFive opportunity overview page detailing the “Suboptimal Azure OpenAI Model Type Selection” savings opportunity, including engineering context, and suggested remediation workflow.

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PointFive “Suboptimal Azure OpenAI Model Type Selection” opportunity analysis page detailing cost data including the “Cost Per Request Over Time” graph referenced above.

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Redefining AI Cost Management Across the Cloud

The next frontier of FinOps is identifying underutilized commitments and suboptimal model mapping. The PointFive Data Fabric establishes a comprehensive visibility layer for the AI stack, enabling a wide range of optimization workflows, such as:

• Automated Attribution: Mapping every token automatically to specific business units or deployments across any provider eliminates the need for manual tagging and complex cost-splitting.
• Capacity Right-Sizing: Identifying discrepancies between "Provisioned" commitments and actual peak usage  removes waste from idle availability.
• Performance-to-Price Optimization: Continuously benchmarking model evolution to identify where newer versions, regardless of vendor, delivers superior unit economics for specific workloads.
• Operational Intelligence: Beyond cost, the platform surfaces usage patterns and telemetry that inform broader infrastructure scaling and performance tuning.

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These are the entry points. The same framework applies to batch vs. real-time pricing tradeoffs, cross-region arbitrage, and emerging efficiency patterns as new models hit the market.

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True AI efficiency requires moving away from "Total Spend" and toward Tokenomics. When your dashboard reflects your actual data assets, "Allocation Magic" becomes your most powerful tool for scaling AI sustainably. 

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Ready to experience PointFive Tokennomics yourself? Book a demo!