Designing cloud-native architectures for AI workloads across AWS, Azure, and GCP. Spans GPU capacity strategy, model-serving topology, data residency, and the c
Cloud architecture for AI designs the platforms intelligence runs on: GPU capacity strategy, inference and training topologies, data gravity management, multi-provider model access, and the cost governance that keeps AI estates affordable at scale.
Premium infrastructure niche: AI spend is the fastest-growing cloud line, and architects who deliver capability with cost discipline are board-visible hires.
GPU scarcity economics, token-metered services, data gravity for retrieval, and a new platform tier (model gateways, vector stores, eval infra), plus cost variance that demands FinOps from day one.
Single-cloud depth with multi-provider model access is the pragmatic 2026 default: infrastructure portability costs more than it returns, while model-layer optionality pays immediately.