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Platform Engineering 2.0: Manage AI Costs and Risks Without Rebuilding

The article discusses the evolution of platform engineering to address AI-related costs and risks. It explores how organizations can manage AI workloads, including cost control and governance, without overhauling their existing infrastructure, by extending platform capabilities to support AI development and deployment.

Background

- **Platform engineering**: an internal IT practice where a team builds a shared "platform" (tools, services, workflows) so other dev teams can ship software without managing infrastructure themselves. It's a hot trend in DevOps. - **Platform Engineering 2.0**: extending that platform to handle AI workloads — GPUs, large models, cost spikes, and new security/regulatory risks around data provenance. - The article's core argument: companies adopt generative AI (LLMs etc.) ad-hoc, leading to ballooning cloud bills, governance gaps, and duplicated work. The fix is to fold AI into the existing platform, not build separate AI infrastructure. - Why it matters: AI adoption is exploding, but most orgs lack the operational maturity to run it cost-effectively and safely. Platform engineering is presented as the natural way to bring order.

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