Architecture Lens

Landing zones make AI experiments safer to scale

AI workloads introduce data movement, model endpoints, private access patterns, token costs, and observability needs that are easy to underestimate. A landing zone gives experimentation a controlled path into production instead of letting each pilot invent identity, network, and logging decisions independently.

  • Separate sandbox, pilot, and production subscriptions or environments with shared policy baselines.
  • Use managed identities, private access where justified, approved data sources, and centralized logging.
  • Tag AI resources by product, environment, owner, model purpose, and experiment or production status.
AI-ready landing zone flow
Identity

Users, workloads, managed identities, and approvals.

Network

Ingress, egress, private paths, and segmentation.

Data

Approved sources, masking, lineage, and retention.

Operations

Logging, cost tags, alerts, and support runbooks.

Original InSkyto diagram informed by Microsoft Cloud Adoption Framework landing zone design areas.

References

Microsoft Cloud Adoption Framework: Azure landing zone design areas Microsoft Azure Well-Architected Framework

Delivery Pattern

Make environment promotion explicit

The path from prototype to production should define the evidence required to promote an AI workload: architecture review, data approval, evaluation results, security controls, cost forecast, and support readiness.

Checklist

Questions to answer before scale

A practical readiness review avoids overbuilding while still making risk visible.

  • Which data sources are approved, and who owns access decisions?
  • Which logs are required for quality, security, cost, and incident response?
  • Which service limits, model costs, and regional constraints could affect launch?