Cloud Solutions: Secure Infrastructure for Production AI
Private endpoints, GPU FinOps, and EU-resident model serving made cloud the default home for enterprise AI workloads in 2026.
Cloud platforms in 2026 were the practical home for production AI — not because hype demanded it, but because security, scale, and operational tooling matured together. European enterprises ran hybrid estates where inference, training, and analytics shared consistent identity, logging, and cost attribution.
Secure AI infrastructure patterns
Private endpoints, workload identity, and network segmentation kept model APIs off the public internet. Secrets rotated through vaults; prompts and outputs logged to immutable stores for audit. Kubernetes on AKS and EKS hosted both traditional microservices and GPU-backed inference pods with autoscaling tuned to business hours.
FinOps for GPU and tokens
AI spend joined traditional cloud FinOps. Teams tagged GPU nodes, reserved capacity for baseline inference, and burst to serverless where latency allowed. Token budgets and model routing — smaller models first, larger only on escalation — kept monthly bills predictable. Sustainability metrics sat beside cost dashboards for leadership reviews.
Compliance-ready operations
EU data residency, encryption in transit and at rest, and backup policies applied equally to vector indexes and relational stores. Guardrails at the gateway enforced content policy before requests reached foundation models. Runbooks covered model deprecation, failover regions, and coordinated patches — the same discipline as any business-critical service.
PrequaliQ designs cloud infrastructure where enterprise AI runs safely — compliant, observable, and cost-aware from the first deployment.