Cloud-native architecture, IaC, Kubernetes, cost optimisation, and multi-cloud strategy.
Everyone benchmarks the accelerator. Almost nobody benchmarks what happens when one of sixteen thousand accelerators stops answering. In large synchronous systems, the unit of failure is the job, not the chip, and the fleet’s real efficiency is set by how fast the layers around the GPU can detect, contain and recover from that fact.
AI workloads expose the limitations of traditional cloud cost management. From token economics and GPU utilisation to idle accelerators, request caching, and model routing, effective FinOps for AI requires measuring costs based on successful outcomes rather than just infrastructure spend. Learn how to optimise inference efficiency, control rising API costs, and establish a continuous framework that balances cost, performance, and quality.
