As organizations move from single-cloud setups to hybrid and multi-cloud strategies, they are under pressure to build data platforms that are both globally available and AI-ready. This talk walks through how to design and operate a global-scale data platform that spans regions and providers, supports multiple storage paradigms (files, object stores, NoSQL, relational), and exposes a clean experience to application teams. We’ll look at how to decouple storage, compute, and AI workloads so analytics, vector search, and LLM inference can run efficiently on shared datasets without creating a new kind of vendor lock-in. Along the way, we’ll cover patterns for embeddings pipelines and vector indexes, approaches for reliability and disaster recovery across regions and failure domains, and cost-management strategies that account for data gravity and GPU-heavy AI workloads. You’ll leave with concrete patterns, trade-offs, and pitfalls to avoid when taking real, messy, business-critical data platforms into an AI-centric, multi-cloud world.