Together with
Mastering AI Economics
Strategies to Manage, Optimize, and Reduce Unit Costs for AI Workloads
Learn how top engineering and FinOps teams are aligning performance with budget by optimizing architecture, tracking true cost per model, and using practical insights to stay ahead of runaway spend.
9th September, 2025
6PM CEST / 12PM EST
Can't You Track your AI costs? We can help
AI workloads are powerful but they’re also expensive. In this webinar, we’ll break down practical strategies to manage, develop, and reduce unit costs across your AI stack.
Whether you’re building LLMs, training models at scale, or just trying to keep your cloud bill sane, this session is packed with actionable tactics you can apply today.
What we’ll cover:
Understanding unit costs: What they are, why they matter, and how to track them
Cost-efficient architecture: Design patterns and trade-offs that lower compute and storage bills
Data & model strategy: How to optimize what you train, when, and where
FinOps for AI: Building transparency and accountability into fast-moving AI teams
Speakers & Hosts
David Gross
Research Director @ GPU Economics
Vaibhav Sharma
FinOps Lead @ Teleperformance
Alon Savo
Product Team Lead @ Finout
