Forecasting AI Investment: Where Smart Conversations Begin

By Amanda Wagner | Trace3 Cloud FinOps Consultant; Brian Moresco | Trace3 Senior Manager, Management Consulting; Colby Buchanan | Trace3 Associate Cloud FinOps Consultant; Luke Myers | Trace3 Associate Cloud FinOps Consultant

As AI adoption accelerates, so do the cloud bills. Training and deploying models, especially at scale, can burn through budgets fast. That’s why forecasting AI spend isn’t just a finance task anymore, it’s a strategic necessity.

Understanding the cost drivers behind compute, storage, inference, and API usage helps teams make smarter decisions, faster. And with cloud providers rolling out new pricing models (like serverless inference and tiered GPU access), staying ahead means tracking both usage and value in real time. ‘You can’t manage what you don’t measure’ has never felt more urgent, especially when your LLM is racking up token charges by the second.

It's therefore essential to think critically about forecasting the costs of generative AI services in the cloud to equip you with the key questions, context, and tools needed to drive meaningful discussions and smarter decision-making across your organization. In service of this goal, you must determine the best practices for a forecasting approach, identify the key metrics that truly matter, and understand the critical lessons learned from managing generative AI costs at scale.

AI Cost Drivers and Challenges:

• What are the biggest cost drivers in AI/ML workloads?
  After a model is trained, the primary cost drivers shift to users and their usage patterns. LLM costs scale linearly, typically based on a fixed rate per token or per 1,000 tokens. The sharp rise in AI-related expenses often stems from rapid user growth and the unpredictability of their interactions and inputs.
  
  
• Predicting cloud spend is already a challenge. What makes AI costs more difficult to forecast than traditional cloud spend?
  Unlike traditional cloud environments, where costs are tied to relatively predictable infrastructure patterns, AI spend scales dynamically with user adoption and fluctuating workloads. This often leads to significant variance between projected budgets and actual costs, creating financial pressure for organizations. Techniques such as time-series analysis, seasonal scaling, and multivariate regression modeling can help reduce cost volatility, though their initial implementation is often hindered by limited historical data.
  
  
• What are some of the blind spots teams encounter when estimating AI costs?
  Teams planning to train their own models often underestimate the cumulative costs of multiple training runs and the data transfer fees associated with moving large datasets. Inefficient prompt design can further drive-up token usage, leading to substantially higher costs.
  
  
• What are the best practices for a team just starting to forecast AI cloud costs?
  For teams just beginning to forecast AI cloud costs, the first step is to establish a baseline using historical data or industry benchmarks. Implement cost attribution and resource tagging early to ensure accurate tracking and visibility as usage scales. Model multiple scenarios to account for volatility and involve FinOps professionals early to provide guidance and foster collaboration. During the initial forecast cycles, regularly review cost variance drivers, i.e., identify patterns and key variables influencing spend to refine future forecast models for improved accuracy.

Forecasting AI costs isn’t just about plugging numbers into a spreadsheet; it’s about fostering cross-functional alignment. Finance wants predictability, engineering wants flexibility, and product wants impact. To bring these perspectives together, start with the right questions.

These prompts can help teams surface blind spots, identify inefficiencies, and align on what really drives value:

• Which AI workloads or products are driving the highest cloud spend?
  Are you spending more on model training, inference, or third-party APIs? Knowing where the money is going helps you prioritize optimization.
• Is there tracking for return on investment (ROI) for AI initiatives by use case?
  For example, if you're using GenAI to automate customer support, are you measuring cost per resolved ticket, time saved, or CSAT impact?
• What does our usage pattern look like - flat, seasonal, or unpredictable?
  Bursty workloads (like fine-tuning or retraining) may require different forecasting and budgeting strategies than steady-state inference.
• Do our teams speak a common language around cost and value?
  If engineering talks about GPU hours and finance talks about dollar amounts, you're bound to miss key signals. A shared vocabulary, like cost per inference or cost per feature, bridges the gap.

These kinds of questions don’t just improve cost visibility, they build a stronger foundation for future AI scaling. They help teams move from reactive cost tracking to proactive planning, aligning technical decisions with business outcomes.

At Trace3, we’ve worked with organizations across industries to tackle this exact challenge. Whether you're just starting to explore AI workloads or looking to optimize and scale responsibly, our team is ready to guide you through the journey, offering the strategy, tools, and hands-on support you need to forecast and manage AI costs with confidence.

If you’re interested in learning more, please don’t hesitate to contact us at finopsteam@trace3.com, or visit our website.