5 Critical Mistakes When Designing Azure High Availability

Introduction

High availability on Azure sounds straightforward until something breaks at 2 AM and you discover your 'redundant' architecture had a single point of failure all along. After years of designing and troubleshooting Azure HA configurations for production SAP and enterprise workloads, the same mistakes keep appearing. They are not exotic edge cases. They are fundamental design decisions that teams get wrong because the defaults feel safe enough, until they are not.

This article covers the five most common Azure high availability mistakes, why they happen, and exactly how to fix them before they cost you an outage.

Mistake 1: Confusing Availability Sets with Availability Zones

This is the most common misunderstanding in Azure HA design. Availability Sets distribute VMs across fault domains and update domains within a single datacenter. They protect against hardware rack failures and planned maintenance windows. Availability Zones, on the other hand, distribute resources across physically separate datacenters within an Azure region. They protect against entire facility-level failures.

The practical difference matters enormously. If you deploy a two-node SQL Server Always On cluster into an Availability Set, both nodes still live in the same building. A power event or cooling failure at that facility takes out your entire cluster. Deploying across Availability Zones means each node sits in a different physical datacenter with independent power, cooling, and networking.

How to fix it

• For production workloads requiring 99.99% SLA, always use Availability Zones where supported
• Reserve Availability Sets for legacy scenarios or regions without zone support
• Check the Azure region's zone support before committing to an architecture. Not all regions offer three zones
• Document which tier of availability protection each workload actually needs based on business impact

Mistake 2: Deploying a Load Balancer Without Health Probes Tuned Correctly

Azure Load Balancer and Application Gateway both rely on health probes to determine whether a backend instance should receive traffic. The default probe settings are generous: they check every few seconds and require multiple consecutive failures before removing a node. In practice, this means a failing backend can continue receiving requests for 15-30 seconds after it becomes unhealthy.

The bigger problem is teams that create probes pointing to a generic HTTP 200 endpoint, like a static page or a root path that always returns OK, instead of a path that actually validates application health. Your probe should check whether the application can reach its database, access required storage, and process a request end-to-end. A VM that returns HTTP 200 from nginx while the backend application has crashed is worse than a VM that is simply offline.

How to fix it

• Build a dedicated health-check endpoint that validates database connectivity, storage access, and critical service dependencies
• Reduce probe interval to 5 seconds with an unhealthy threshold of 2 for faster failover
• Use application-layer probes (HTTP/HTTPS) instead of TCP probes whenever possible
• Test failover by intentionally killing a backend process and measuring how long until traffic reroutes

Mistake 3: Ignoring Dependency Chains in Multi-Tier Architectures

You can have perfectly redundant web servers sitting behind a load balancer, distributed across three Availability Zones, and still experience a full outage. How? Because every one of those web servers depends on a single database instance, a single Redis cache, or a single storage account that has no redundancy at all.

HA design is only as strong as the weakest link in your dependency chain. A real-world example: a client had a fully zone-redundant application tier with auto-scaling, but their configuration files lived on a single Azure File Share with LRS (Locally Redundant Storage). When the storage cluster hosting that share experienced an issue, every application instance failed to start on reboot because they could not mount their config volume.

How to fix it

• Map every upstream and downstream dependency for each component in your architecture
• Apply the same availability tier to shared dependencies as you apply to the services that consume them
• Use ZRS (Zone-Redundant Storage) for any storage account that serves configuration, state, or shared data in a zone-redundant architecture
• Architect databases with Always On Availability Groups, Cosmos DB multi-region, or Azure SQL zone-redundant deployments depending on your data platform

Mistake 4: Skipping Failover Testing Under Realistic Conditions

This is the one that bites hardest. Teams invest weeks designing an HA architecture, deploy it, run a quick smoke test, and move on. Then, six months later when an actual failure occurs, the failover process takes three times longer than expected, data replication was behind, and the runbook references a portal experience that has since changed.

Failover testing is not a one-time deployment validation. It is a recurring operational exercise. Azure Site Recovery has a test failover feature specifically designed for non-disruptive DR drills. Azure Chaos Studio lets you inject faults into running infrastructure to validate resilience. Neither of these costs much to run, but the information they provide is invaluable.

How to fix it

• Schedule quarterly failover drills with full team participation, not just the infrastructure team
• Use Azure Site Recovery test failover to validate DR readiness without impacting production
• Document actual failover times and compare against your RTO commitments
• After each drill, update runbooks with any steps that were missing, unclear, or outdated
• Simulate failures during business hours with stakeholder awareness to test real incident response

Mistake 5: Treating Cost Optimization and HA as Opposing Goals

Budget pressure leads teams to cut HA corners. Running a single instance instead of two. Using LRS instead of ZRS. Skipping the secondary region for DR. These decisions save money in the short term and create enormous risk exposure. But the opposite extreme, over-engineering every component for maximum redundancy regardless of business criticality, wastes budget that could be invested in the workloads that actually matter.

The right approach is tier-based availability. Not every workload needs 99.99% uptime. A dev/test environment does not need zone redundancy. An internal reporting tool might tolerate 30 minutes of downtime. But your customer-facing transaction system absolutely needs multi-zone deployment with automated failover and tested DR.

How to fix it

• Classify workloads into availability tiers: mission-critical, business-important, and standard
• Define SLA targets, RTO, and RPO for each tier and match Azure service configurations accordingly
• Use Azure Advisor and Cost Management to identify over-provisioned HA components in lower-tier workloads
• Present HA investment decisions in terms of business risk: compare the cost of redundancy against the estimated cost of downtime per hour

Conclusion

Azure gives you the building blocks for highly available architectures, but the platform will not stop you from assembling them incorrectly. Every mistake on this list comes from real production incidents. Systems that looked resilient on a whiteboard but failed under actual conditions.

The common thread is that high availability is not a feature you enable. It is a practice you maintain. Design with zones, test your failovers, map your dependencies, tune your health probes, and right-size your availability investments to match actual business requirements. Do those five things consistently, and you will avoid the outages that catch most teams off guard.