What AI changes in cloud architecture
Inference latency dictates user experience. Inference cost dictates unit economics. Both run at different sensitivities than traditional cloud workloads, and both compound at scale.
Modern cloud estates need GPU strategy (managed model endpoints vs reserved capacity vs on-prem), inference caching strategy, and a clear posture on data egress (where inference physically happens).
FinOps for AI
Token-level cost reporting is now table stakes. So is per-task economics: cost per claim handled, cost per document summarised, cost per customer-service interaction.
We build FinOps dashboards that show the per-task economic envelope and surface drift early. Most large clients see 20 to 35 percent lower AI run cost within four quarters once FinOps practice matures.
Sovereign and on-prem
For regulated workloads, sovereign cloud (AWS European Sovereign, Azure Government, Google Sovereign Controls) and dedicated on-prem GPU clusters are increasingly the right answer.
We deliver these without religious attachment to any single approach. The architecture follows the regulatory and operational constraint.