How do you design a multi-region, cost-efficient Azure HA?

Long Answer

A resilient Azure high-availability architecture spanning multiple regions balances four forces: reliability, latency, operability, and cost. The blueprint below organizes the stack into edge, stateless compute, data, and operations—then adds concrete levers to keep spend in check while preserving SLOs.

1) Edge and traffic management

Front the application with Azure Front Door Standard/Premium to get anycast IPs, global load balancing, health probes, path-based routing, and a built-in WAF. Pair with Azure CDN for static assets and cacheable API GETs. Configure origin groups with two primary regions (e.g., West Europe, North Europe) in active-active. Health probes remove an unhealthy region automatically; priority and latency routing let you choose active-passive or balanced traffic.

2) Regional stateless compute

Pick one:

• App Service for PaaS web workloads; enable Autoscale by CPU/RPS, minimum instances per region to meet cold-start SLOs.
  
• AKS for containerized services; use cluster-autoscaler, spot node pools for stateless jobs, and pod disruption budgets.
  
• Azure Functions (Premium/Elastic) for event-driven endpoints with warm concurrency.
  

Keep services stateless. Use JWTs or Redis for session, and store files in Azure Storage (GZRS/RA-GZRS) rather than local disks. Define identical deployments per region (IaC) so failover is predictable.

3) Data and consistency options

Choose the lowest-cost data tier that still meets RPO/RTO:

• Azure SQL Database with Auto-failover groups: a primary in Region A and a geo-secondary in Region B. Reads can target the secondary to lower latency for read-heavy pages. RPO ~5s depending on log rate; RTO minutes. Good for transactional apps that don’t need cross-region writes.
  
• Cosmos DB for multi-region reads and optional multi-region writes with tunable consistency (Session/Strong). Place replicas near users; use autoscale RU/s and TTL on hot collections to trim spend. Ideal for global, low-latency read patterns or multi-master needs.
  
• Azure Cache for Redis per region to offload hot keys, sessions, feature flags, and rate-limit counters. Choose Geo-replication only when necessary; otherwise warm each region independently to save cost.
  
• Blob Storage with GZRS/RA-GZRS for static/media; lifecycle policies push cold data to Cool/Archive tiers.
  

4) Messaging and async resilience

Introduce Event Hubs or Service Bus for durable messaging between services, and Storage Queues for simple fan-out. This decouples spikes, protects the database, and gives natural back-pressure. For background work, run Functions or AKS jobs with idempotent handlers so replay after a failover is safe.

5) Identity, networking, and zero trust

Use Managed Identity everywhere; authorize by Azure RBAC and service endpoints/private endpoints to keep traffic on the virtual network. For AKS/App Service to data, prefer Private Link. Egress via NAT Gateway. Segment by subscriptions and resource groups for blast-radius control.

6) SLOs, autoscale, and failover drills

Define SLOs (e.g., availability 99.95%, p95 < 250 ms). Configure autoscale metrics (RPS/CPU/queue depth), with conservative min instances to absorb bursts. Write runbooks for failover: promote SQL secondary, update Front Door priority, invalidate caches, and confirm health probes. Run monthly game days that simulate region loss and validate RTO/RPO.

7) Observability and ops

Centralize telemetry in Azure Monitor/Log Analytics; emit distributed traces via OpenTelemetry. Use Application Insights for dependency maps, live metrics, and alerts on error-budget burn. Add Availability tests from multiple geos. Keep per-region dashboards (latency, 5xx, saturation) and tie alerts to incident channels.

8) CI/CD and IaC

Bake everything as code with Bicep/Terraform. Build images in Azure Container Registry; deploy with GitHub Actions or Azure DevOps using blue-green/canary slots (App Service) or staged rollouts (AKS). Parameterize regions and SKUs so you can scale down a secondary during off-peak if business can tolerate slower RTO overnight.

9) Cost controls without losing HA

• Right-size SKUs and set autoscale with realistic floors.
  
• Use Front Door + CDN to reduce egress/origin load.
  
• Prefer read-only geo replicas over full multi-master when writes are regional.
  
• Turn on Cosmos autoscale and partition well to avoid hot shards.
  
• Apply lifecycle policies on Blob, log sampling, and shorter retention in Log Analytics.
  
• Use Reserved Instances/Savings Plans only for steady baseline capacity; keep burst capacity serverless or autoscaled.
  

This design yields a pragmatic, multi-region Azure architecture with high availability and disciplined cost posture: global edge, duplicated stateless compute, the right data topology, and guardrails that keep both reliability and budget healthy.

Table

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Common Mistakes

• Single-region design behind a global DNS—no real failover.
  
• Sticky sessions on instance memory; region loss logs users out or corrupts carts.
  
• Choosing Cosmos multi-write when one-region write + replicas suffices (unneeded RU/s cost, complex conflict rules).
  
• Oversizing App Service plans “just in case” instead of autoscaling with a safe floor.
  
• No health probes or wrong probe path; Front Door never fails over.
  
• Treating Redis as a database; missing persistence/eviction rules.
  
• Chatty cross-region calls (compute in EU, DB in US) inflating latency and egress.
  
• Ignoring DR runbooks; first failover happens during a real incident.
  
• Infinite log retention in Log Analytics driving surprise spend.

Sample Answers (Junior / Mid / Senior)

Junior:
“I’d place Azure Front Door in front of two regions running App Service. Sessions would be stateless or in Redis. Data uses Azure SQL with Auto-failover to a secondary. CDN serves static files. Autoscale keeps cost down.”

Mid:
“Active-active Front Door origins (WE/NE). App Service with autoscale and per-region Redis. Azure SQL failover group with read-intent routing; blob on RA-GZRS and CDN. Service Bus for async jobs. IaC with Bicep; canary slots reduce risk and spend.”

Senior:
“Front Door + WAF + CDN at edge. Compute on AKS across two regions with spot node pools for stateless pods. Data split: Cosmos for global reads (Session consistency), SQL for transactional with failover group. Per-region Redis, Private Link, Managed Identity. SLOs drive min-capacity; autoscale handles bursts. App Insights with error-budget alerts; monthly failover drills. Reserved for baseline only.”

Evaluation Criteria

• Edge: Front Door configured with correct health probes, WAF, and routing policy.
  
• Regions: At least two, documented active-active/active-passive behavior.
  
• Compute: Stateless services, autoscale, and right-sizing (App Service/AKS/Functions).
  
• Data: Proper choice (SQL failover groups vs Cosmos); RPO/RTO stated; read replicas used wisely.
  
• Cache: Per-region Redis with TTL/eviction policy.
  
• Async: Service Bus/Event Hubs for spikes and resilience.
  
• Security/Networking: Managed Identity, Private Link, segmented VNets.
  
  

Ops: App Insights, SLOs, burn-rate alerts, DR runbooks and drills.Red flags: Single region, sticky in-memory sessions, cross-ocean DB calls, overprovisioned SKUs, Cosmos multi-write without need.

Preparation Tips

• Pick two Azure regions close to users plus a DR candidate; confirm quotas.
  
• Prototype Front Door → App Service in both regions; validate health-probe failover.
  
• Make the app stateless; move session/files to Redis/Blob.
  
• Stand up Azure SQL with Auto-failover group; run a planned failover and measure RTO/RPO.
  
• If global reads dominate, add Cosmos with autoscale and Session consistency.
  
• Add CDN and define cache keys/TTL for APIs and assets.
  
• Wire Service Bus for heavy tasks; ensure idempotent handlers.
  
• Instrument with Application Insights; create SLO dashboards and burn alerts.
  
• Codify in Bicep/Terraform; use blue-green slots or staged AKS rollouts.

Real-world Context

• Retail EU: Moved from DNS-only failover to Front Door active-active over WE/NE. With CDN and Redis, p95 fell 30% and origin egress dropped 45%.
  
• Fintech: Azure SQL failover groups + read replicas; monthly drills cut RTO to ~3 min. Costs controlled by autoscale and pausing dev replicas at night.
  
• SaaS analytics: Cosmos multi-region reads (Session), writes centralized; autoscale RU/s saved ~28% vs fixed.
  

Media: AKS with spot pools for stateless workers; Savings Plans for baseline API nodes. App Insights burn-rate alerts prevented two SLO breaches during traffic spikes.

Key Takeaways

• Front Door + WAF + CDN for global ingress and smart failover.
  
• Duplicate stateless compute per region; cache locally with Redis.
  
• Choose SQL failover groups or Cosmos based on consistency needs.
  
• Use autoscale, storage tiering, and RU autoscale to control cost.
  
• Practice failovers; let SLOs set your minimum capacity.

Practice Exercise

Scenario:
Your product must achieve 99.95% availability across Europe with NA users as a secondary audience. You expect traffic spikes during promotions and mostly read-heavy workloads. Budget pressure is high.

Tasks:

1. Pick two primary regions and one DR region. Define an Azure Front Door origin group with health probes and priority/latency routing.
   
2. Choose compute (App Service vs AKS vs Functions) and justify. Specify autoscale triggers (RPS/CPU/queue).
   
3. Select the data tier: Azure SQL + Auto-failover group with read-intent routing, or Cosmos DB with multi-region reads (Session). Explain RPO/RTO and cost trade-offs.
   
4. Add Azure Cache for Redis per region. Propose cache keys/TTLs for product pages and API GETs.
   
5. Put static/media on Blob (RA-GZRS) behind CDN with cache rules.
   
6. Introduce Service Bus for async jobs; outline idempotent handlers and DLQ policy.
   
7. Instrument with App Insights and create SLO dashboards and burn-rate alerts.
   
8. Define a DR runbook: SQL planned failover (or Cosmos write region switch), Front Door priority flip, cache warmup, verification checks.
   
9. Provide cost levers you’ll turn first if spend rises 20% (autoscale floors, RU autoscale, blob tiering, log retention).
   
   

Deliverable:
A concise 1–2 page Azure diagram + bullets explaining how the design meets HA targets while keeping costs under control.