Flexible Telecom AI Workload Deployment Across AWS Hybrid Cloud

This article presents a structured framework for deploying AI workloads across AWS hybrid cloud infrastructure in telecommunications networks.

• Evaluate AI lifecycle phases (training, context creation, inference, optimization) against four dimensions: data sovereignty, latency, data gravity, and operational readiness
• Training is cloud-native; context creation follows data gravity; inference placement is most complex; optimization spans hybrid infrastructure
• AWS provides four infrastructure tiers: Regions (scale), Local Zones (metropolitan edge), Outposts (on-premises), and AI Factories (sovereign-scale)
• API consistency across tiers enables workloads to move without code changes using EKS and Amazon Bedrock
• 5G scenario: edge SLM semantic filtering reduces cloud inference tokens by 90%, satisfying sovereignty, latency, and data gravity constraints simultaneously
• Four-step maturity path: data classification, latency instrumentation, token cost modeling, and infrastructure evaluation

Structured placement decisions replace open-ended design choices, enabling resilient architectures that respect regulatory, latency, and data gravity constraints.