AI Total Cost of Ownership: Strategies for Smarter TCO Management
- Nan Braun
- 1 minute ago
- 3 min read
Implications of AI Total Cost of Ownership (TCO)
For ITFM / TBM analysts, AI fundamentally reshapes cost distribution across an application's lifecycle. Unlike traditional applications, where costs accrue gradually, AI models demand up to 80-90% of their total compute cost before deployment. This is driven by the immense computational power required for AI training, which far exceeds the ongoing costs of running the model in production.
While cloud computing has traditionally enabled a shift from capital expenditures (CapEx) to operational expenditures (OpEx), AI disrupts this trend. The significant investments in AI training should be capitalized and amortized over the application’s lifespan, much like software development labor costs.
This shift impacts the total cost of ownership (TCO) in several key ways:
Capitalization Policies: Organizations must determine which AI development costs should be capitalized versus expensed.
Cost Amortization: Training costs should be distributed across the application's lifecycle to better reflect long-term value.
Cloud vs. On-Premises: While cloud training offers flexibility, heavy AI workloads may justify investment in on-premises GPU infrastructure.
Managing Ongoing Costs: AI applications can optimize costs post-deployment through serverless AI services or efficient model inference.
As AI adoption grows, financial models must evolve to ensure AI investments remain cost effective and strategically sound.
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Communicating The Cost and Value of AI
AI Cost Allocation Strategies
Like other IT Services, AI cost allocation should mature toward a consumption-based model. Tagging AI service and infrastructure usage - whether on-premises or in the cloud – is critical for accurate cost allocation.
Gen-AI cost allocation mirrors cloud cost strategies, aiming to tie expenses directly to AI usage. Generative AI models in the cloud rely on token consumption, where increased compute usage escalates token costs. Tracking and optimizing token usage is essential for managing expenses.
AI models follow a two-phase cost structure:
1.      Training Phase: High, short-term computational demands.
2.      Inference Phase: Lower, continuous resource consumption.
Tagging resources separately for training and inference improves cost visibility and allocation accuracy.
Some AI applications serve multiple use cases across various teams, necessitating shared infrastructure models. Consumption-based allocation, like non-AI shared services, ensures fairness and transparency.
Reserved Capacity for AI
Pre-allocating computation resources (e.g., GPUs) for generative AI can reduce costs and ensure availability. In the cloud, reserved capacity provides cost savings over on-demand pricing and aids in planning for peak usage periods. On-premises, utilization of reserved resources must be balanced against the capital costs.
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Sustainability Considerations
AI’s growing computational demands impact power consumption and sustainability goals. For some organizations, this has increased energy needs driven discussions of data center expansion.
However, emerging AI models challenge the assumption that high compute power is essential. Deepseek, a new AI chatbot, reportedly required just one-tenth of the compute power compared to Meta’s Llama 3.1 model. While independent verification is pending, such advancements suggest AI efficiency improvements may mitigate sustainability concerns.
By making smart choices about the computational power needed for the problem at hand, organizations can align AI adoption with sustainability targets.
AI Financial Metrics and Reporting
Key Metrics and KPIs
The FinOps Foundation provides KPI recommendations for all FinOps capabilities and unit cost metrics.Â
Essential metrics include:
•    Cost per Token: Tracking token usage to monitor spending.
•    Compute Utilization Rate: Measuring efficiency of AI model execution.
•    Training Time Cost: Evaluating the expense of model training periods.
•    Inference Cost per Request: Understanding ongoing operational costs.
•    Storage and Data Transfer Costs: Assessing the financial impact of AI data management.
Monitoring these KPIs enhances cost transparency and enables proactive AI financial optimization.
Budgeting/Forecasting
Given AI’s nascent stage, tracking spend variances will be crucial for learning and optimization. Initial variances—potentially large—should inform best practices rather than serve as performance assessments. Organizations must refine budgeting and forecasting models based on real-world AI cost data over time.
TCO Cost Management and Reporting
Managing AI’s Total Cost of Ownership is critical, especially when AI components contribute to larger business applications. AI-driven insights should integrate into existing dashboards, providing a clear view of AI’s financial impact on broader business services.
Cloud Unit Economics and AI
The FinOps Foundation defines Cloud Unit Economics as a system for maximizing profitability through objective financial measurement. In AI contexts, this translates to:
•    Cloud Cost per Processed Insurance Claim
•    Cloud Cost per New Product Designed
•    Cloud Cost per Goods Sold
•    Cloud Cost per Customer Acquisition
Developing these metrics requires strong collaboration between finance, IT, and business teams, supported by robust data governance.
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