AI is in production now what does it cost you

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Moving AI from an R&D experiment to a production-grade service introduces a new layer of infrastructure complexity. For SRE and DevOps teams, the challenge isn't just performance—it's the sudden lack of visibility into shared GPU clusters, token-heavy API calls, and the "tagging debt" that follows.

Join Google Cloud, Shopify, and Finout for a live session on how teams are handling this in practice.

What we’re covering:

From Experiments to Unit Economics: Architecture patterns for moving from "just ship it" to a governed, scalable production model.
The Attribution Gap: How to map token usage and GPU clusters to specific services without manual tagging or breaking CI/CD pipelines.
Infrastructure Accountability: Frameworks for answering executive questions on AI ROI with data that actually holds up to scrutiny.

Speakers:

Eric Lam, Head of Cloud FinOps, Google Cloud
Chase Platon, Sr. Staff Technical Program Manager, Shopify
Roi Ravhon, Co-Founder & CEO, Finout

When: Tuesday, April 14 12 PM EST | 9 AM PST

[Register here]

This is a peer-level discussion focused on the architectural hurdles of 2026. No fluff, just the frameworks being used by the world’s largest cloud consumers.