Event details
What does intelligence really cost at production scale?
As AI workloads move from the lab to production, the economics of intelligence has become one of the most consequential factors platform leaders face.
Join us for a discussion of Signal65’s in-depth economic study of training and inference costs across CoreWeave and leading hyperscalers. The analysis examines cloud economics, performance benchmarks, and multi-year total cost of ownership to show how infrastructure choices affect the economics of AI over time.
Going beyond simple compute pricing, this session explores the hidden costs that compound at scale and how infrastructure economics translate directly into token costs at production volume. You’ll leave with a practical framework for evaluating fully loaded economics across the complete training-to-inference lifecycle.
In this webinar, we'll cover:
- How training and inference costs compare across CoreWeave and leading hyperscalers
- What performance benchmarks reveal about the relationship between infrastructure and AI economics
- Which hidden costs can compound as workloads scale from development to production
- How infrastructure decisions translate into token costs at production volume
- How to evaluate fully loaded TCO across the training-to-inference lifecycle
- What platform leaders should consider when comparing infrastructure options for long-term AI workloads
Understand the full economic picture behind AI infrastructure decisions. Register now.
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