Hybrid Cloud & Edge Infrastructure on Azure

Centralised orchestration with federated execution of AI workloads across multi-cloud environments
Design Intent

This design assumes a control-plane-led operating model where orchestration ownership is centralised in Azure while execution is distributed across environments. Governance is enforced through identity-first access and policy discipline, ensuring consistent control across clouds. Consumption follows a placement-driven model where workload execution is determined by enterprise constraints rather than platform affinity. Azure provides the execution context, anchored through Microsoft Entra ID and Azure Policy to maintain identity integrity and governance consistency.

Design
Design Walkthrough
  • Centralising orchestration and control ensures all workflow decisions originate from a single governance boundary, preventing fragmented execution logic across clouds (Logic Apps, Event Grid, Microsoft Entra ID, Azure Policy)
  • Establishing a shared data hub with optional movement paths reduces duplication while preserving flexibility, preventing inconsistent datasets across environments (Azure Data Lake Storage Gen2, Azure Data Factory)
  • Standardising execution through container platforms enables true workload portability, preventing dependency on any single environment and enabling vendor-neutral execution (AKS, EKS, GKE)
  • Introducing explicit workload placement logic enforces policy-based routing rather than ad hoc deployment decisions, preventing uncontrolled cost or compliance drift (AKS, EKS, GKE)
  • Securing interconnects between environments ensures controlled communication paths, preventing exposure of data flows across cloud boundaries (VPN / Private Networking)
  • Embedding observability and governance as a cross-cutting layer ensures consistent visibility and auditability, preventing blind spots in multi-cloud operations (Azure Monitor, Log Analytics, OpenTelemetry, Azure Policy)
Operational Outcomes
Enables
  • Central control over distributed AI execution across environments
  • Consistent identity and policy enforcement across cloud boundaries
  • Portability of workloads without platform lock-in
  • Unified monitoring and audit visibility across all environments‍
Good fit when
  • Workloads must operate across multiple cloud providers
  • Regulatory or latency constraints influence workload placement
  • Enterprises require flexibility without sacrificing governance
  • Cross-cloud data consistency must be maintained
This reference architecture reflects patterns we see when enterprises attempt to standardise platforms while still allowing teams to move at different speeds.

A practical way to understand whether our approach fits your operating reality.

© 2026 Chavan. All rights reserved
© 2026 Chavan. All rights reserved