Platform Engineering 1.0 is hitting its ceiling on two critical fronts. Current platforms have no native support for GPU workloads, model serving, MCP servers, or governed agentic operations — making them a structural barrier as AI moves from experiment to production. At the same time, FinOps remains reactive, surfacing spend after budgets are already committed rather than enforcing financial discipline at the moment decisions are made.

This session covers two of the five pillars of Platform Engineering 2.0, delivered through VMware Cloud Foundation (VCF). On the AI-Native front, VCF establishes the platform as the enterprise substrate for AI: enabling 1-click GPU provisioning, model registries, and ML pipelines alongside traditional workloads, while governing AI agents as policy-bounded platform citizens under the same control plane — with least-privilege access, scoped permissions, and continuous audit trails. On the Embedded FinOps front, VCF moves cost intelligence upstream through pre-deployment cost gates, real-time showback and chargeback per tenant, and AI-specific Tokenomics that attribute GPU time, token consumption, and inference costs per team and per model — all enforced by a budget policy engine before services reach production.

Join this session to learn how VCF delivers both capabilities on a single control plane — and what it takes to build infrastructure that is genuinely ready for the agentic AI era.

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