Azure Infrastructure

Azure-First Architecture

A Microsoft Azure-native AI platform engineered for Rogers — Azure OpenAI and Azure AI Foundry at the core, Entra ID and Key Vault for governance, AKS and Container Apps for hosting. Built for speed without shortcuts: time-boxed POCs, hard go/no-go gates, and pre-built Azure delivery patterns.

Modular Stack Agentic Flow Deployment Models Comparison Container Tooling Trustworthy AI Testing Framework
Architecture Diagrams

AI Stack Overview

The Azure-native modular stack for AI solutions.

Azure-Native Architecture

AI in a Box — Azure-Native Stack

Every layer built on Microsoft Azure services with flexibility for best-of-breed partner tools.

Color key Entry Point (UI) API Management Orchestration Data & Content Models Connectivity & Tools Infrastructure Safety & Guardrails Security & Access Governance Evaluation
End-to-End Agentic Flow

Agentic AI Architecture

From user request through orchestration, reasoning, and response

Flexible Deployment

Three Deployment Models

Every AI solution has two core components: the AI intelligence layer and the application hosting layer. We offer flexible deployment options that adapt to your security requirements, infrastructure maturity, and business goals.

Cloud Deployment

Fastest path to production — leverage enterprise-grade AI services and flexible cloud hosting with minimal infrastructure overhead.

AI Intelligence

Azure OpenAI & Azure AI Foundry

  • Access GPT-5, GPT-4o, Claude, Phi-4, and Llama models through Azure OpenAI and Azure AI Foundry
  • Models are consumed via Azure endpoints — no model hosting or GPU management required
  • Enterprise data stays within your Azure tenant, protected by Entra ID and Private Link
  • Scales on demand across Azure regions with Microsoft's enterprise SLA

Application Hosting

Azure App Service, AKS, or Container Apps

  • Deploy on Azure App Service or Azure Container Apps for serverless simplicity
  • Use Azure Kubernetes Service (AKS) or OpenShift on Azure for advanced scaling and management
  • Databases run as Azure SQL, Azure Cosmos DB, or Azure Database for PostgreSQL — fully managed
  • All traffic flows through Azure Front Door and Azure API Management with Entra ID auth
Best for: Organizations standardizing on Microsoft Azure who want the fastest path to production using managed Azure services and Azure OpenAI.

Hybrid Deployment

Azure AI services in the cloud, applications deployed across Azure and on-premises — connected through Azure ExpressRoute or Private Link.

AI Intelligence

Azure OpenAI & Azure AI Foundry (PaaS)

  • AI models provisioned as managed services in Azure OpenAI and Azure AI Foundry
  • Data stays within your Azure tenant — governed by Entra ID, Azure Policy, and Microsoft Purview
  • No model hosting or GPU management required — Microsoft handles availability and updates
  • Fine-tuning, custom models, and Azure AI Content Safety available out of the box

Application Hosting

Azure or On-Prem Data Center

  • Host on AKS, Azure Container Apps, or on-premises OpenShift clusters
  • Applications connect to Azure OpenAI via Azure Private Endpoints — no public internet traversal
  • Databases can be Azure-managed (Azure SQL, Cosmos DB) or self-hosted on-prem
  • Azure ExpressRoute or VPN Gateway bridges on-prem workloads to Azure AI services
Best for: Organizations with existing on-premises infrastructure that want managed Azure AI services while keeping select workloads in their own data center.

On-Premise Deployment

Full control over your AI stack — run your own models on dedicated hardware with complete data sovereignty.

AI Intelligence

Self-Hosted OSS Models (or Azure Sovereign)

  • Run open-source models (Llama, Phi-4, Mistral) on your own GPU infrastructure or on Azure Stack HCI
  • Requires dedicated GPU hardware (NVIDIA H100/H200) — on-prem or on Azure ND H100 v5 VMs
  • Models served internally via vLLM or NVIDIA Triton — no data leaves your network boundary
  • Azure Sovereign Cloud or Azure Government available for strict data residency requirements

Application Hosting

Azure Stack HCI or On-Prem Data Center

  • Applications deployed on Azure Stack HCI, Azure Local, or your on-prem OpenShift/Kubernetes cluster
  • Arc-enabled Kubernetes brings Azure management and governance to on-prem clusters
  • All components — models, application services, and databases — run behind your firewall
  • Full ownership of the stack with optional Azure Arc for hybrid visibility
Best for: Organizations with strict data sovereignty requirements, air-gapped environments, or regulated industries that need complete control over AI models and infrastructure.
At a Glance

Deployment Model Comparison

Cloud
Hybrid
On-Premise
AI Models
Azure OpenAI (GPT-5, GPT-4o)
Azure OpenAI + Azure AI Foundry
Self-hosted OSS (Llama, Phi-4, Mistral)
GPU Hardware
Not required
Not required
Required (NVIDIA H100/H200)
App Hosting
Azure App Service / AKS / Container Apps
AKS on Azure or OpenShift on-prem
Azure Stack HCI or On-Prem OpenShift
Data Residency
Azure region (Canada East/Central)
Your Azure tenant + on-prem
Fully on-premise / Azure Sovereign
Time to Deploy
1 Week
2 Weeks
1 months to 4 months (hardware procurement)
Best For
Speed & simplicity
Governed cloud environments
Regulated / air-gapped
Container Tooling

Hosting Software & Required Tools

The software stack required to build, deploy, and operate containerized AI solutions across any deployment model.

Category
Tool / Software
Purpose
Container Runtime
Podman / Docker
Build and run OCI-compliant container images
Container Registry
Azure Container Registry (ACR)
Store and distribute container images securely in Azure
Orchestration
Azure Kubernetes Service (AKS) / OpenShift on Azure
Schedule, scale, and manage container workloads in production
Cluster CLI
oc (OpenShift) / kubectl
Deploy, inspect, and manage workloads and cluster resources
Package Management
Helm
Template, version, and deploy Kubernetes manifests as reusable charts
Web Server
Nginx (unprivileged) / Envoy
Serve static assets and reverse-proxy API traffic
Base Image
Alpine Linux / UBI (Red Hat)
Minimal, security-hardened OS layer for containers
CI / CD Pipeline
Azure DevOps / GitHub Actions
Automate build, test, scan, and deploy workflows
Image Scanning
Microsoft Defender for Containers / Trivy
Scan images for CVEs and policy violations before deployment
Secrets Management
Azure Key Vault (with Managed Identity)
Inject API keys, credentials, and certificates securely at runtime
Persistent Storage
Azure Files CSI / Azure Disks / Azure NetApp Files
Provide shared and persistent volumes for stateful workloads
Observability
Azure Monitor + Application Insights + Log Analytics
Monitor container health, logs, metrics, and alerting
Service Mesh / Ingress
Azure Application Gateway / Istio / Nginx Ingress
Route external traffic, TLS termination, and mTLS between services
GPU Serving
Azure ND H100 v5 VMs / NVIDIA Triton / vLLM
Serve LLM inference workloads on GPU-equipped Azure or on-prem nodes
AI Governance

Trustworthy AI Models at Scale

As models move from pilot to production, organizations face challenges around data quality, explainability, drift, and compliance. A structured testing and monitoring framework ensures AI systems remain reliable, fair, and compliant across their lifecycle.

Challenge

Scaling AI models across assets and operations introduces data quality issues, bias, and performance drift that reduce confidence in model outputs.

Inconsistent Data Quality

Incomplete or biased data lowers accuracy

Limited Explainability

Hard to understand or qualify model outputs

Model Drift Over Time

Changing conditions reduce performance

Scaling Risk

Models fail when moving from pilot to production

Compliance Pressure

Regulators demand transparency & accountability

Solution Approach

A structured approach tests and monitors models to ensure they remain reliable, transparent, and aligned with business goals and compliance.

Testing Framework

Structured validation of models and data for reliability

Explainability & Fairness

Make model logic clear and unbiased

Continuous Monitoring

Detect drift and refresh models regularly

Scalable Assurance

Embed testing into MLOps for consistent performance

Governance & Documentation

Maintain records for oversight and audits

Pre-Production Validation

AI Model Testing Framework

A structured, pre-production framework for validating AI systems. Ensures reliability, resilience, and regulatory alignment before deployment. Embeds Responsible AI principles across technical, operational, and compliance layers.

Objectives & Key Activities

Quality & Reliability

Ensure the AI model is accurate, reliable, and ethical before release.

Quality validation: Verify model accuracy, stability, and reproducibility across diverse datasets.

Bias and fairness analysis: Detect and reduce bias across demographic, regional, or input groups.

Explainability & Transparency: Ensure model logic and outcomes are interpretable and traceable.

Resiliency & Performance

Test how the model performs under real-world conditions in a controlled setting.

Stability & Stress Testing: Assess performance under noise, load fluctuations, and outlier scenarios or instability.

Performance Drift Simulation: Test adaptability to future or shifting data patterns.

Adversarial & Robustness Testing: Evaluate resilience to manipulated or extreme inputs.

Compliance Review

Ensure the model complies with internal policies, ethics, and regulations before release.

Deployment Approval: Present validation outcomes to governance or AI risk committees for go/no-go decisions.

Independent Review: Confirm readiness through internal audit or third-party assessment.

Owners

Led by first-line development teams: Developers, and product owners.

Jointly managed by first-line operational teams and second-line risk/governance functions to validate deployment readiness.

Managed by Risk, Compliance teams and third-party independent reviewers for unbiased assurance.