On-demand webinar
Mastering AI Economics: Reducing Unit Costs for AI Workloads
In this webinar
AI workloads offer transformative power, but without proper control, they can become costly overnight. In this workshop, FinOps leaders show how to design architectures, track costs per model, and put in place best practices to reduce unit costs in real-world environments.
Key takeaway
- What unit costs are in AI workloads and why measuring them matters
- Architecture design patterns and storage/compute trade-offs for cost efficiency
- Data & model strategy: when (and how much) to train, where to train, and how to avoid overuse
- FinOps best practices for fast-moving AI teams: transparency, accountability, and reducing waste
- How to use practical reporting & cost visibility to keep budgets aligned with performance
Your speakers
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Alon Shvo Product Team Lead
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David Gross Research Director
GPU Economics -
Victor Garcia Founder
FinOps Weekly -
Vaibhav Sharma FinOps Lead
Teleperformance
