Public Web and API Operations and Reliability¶

Public workloads are judged by external availability first, so operations must connect traffic health, deployment safety, and regional recovery into one model. [Validated]

SLO targets¶

Target SLOs should match business criticality rather than default to generic “three nines” language.

Health checks and autoscaling¶

• Use health endpoints that represent application readiness, not just process liveness. [Documented]
• Scale on signals tied to user experience such as request concurrency, CPU saturation, queue depth, or latency trends. [Observed]
• Treat autoscale as a way to absorb demand variability, not as a substitute for capacity planning. [Validated]

Deployment safety¶

Deployment slots, staged traffic shifts, and rapid rollback paths are part of the baseline operating model for public web workloads. [Documented]

Good practices:

• Warm new instances before switching traffic. [Observed]
• Separate schema-breaking changes from application rollout when possible. [Correlated]
• Measure error budget impact of release frequency, not only deployment speed. [Validated]

Disaster recovery strategy¶

The DR model should align with dependency topology:

• Single-region with restore for lower criticality workloads. [Documented]
• Active-passive multi-region when recovery time matters more than simultaneous global read locality. [Inferred]
• Active-active multi-region only when business benefit justifies conflict handling, cache design, and operational overhead. [Observed]

Reliability control loop¶

flowchart LR
    A[User traffic and synthetic probes] --> B[Health checks and telemetry]
    B --> C[Autoscale and deployment decisions]
    C --> D[Runtime capacity and routing state]
    D --> E[Regional failover or rollback]
    E --> B

Observability expectations¶

• Capture request, dependency, exception, and platform metrics in one operational view. [Documented]
• Use synthetic tests from outside the application region to detect internet path failures. [Validated]
• Correlate edge logs with origin telemetry so WAF blocks, latency, and backend saturation can be reviewed together. [Correlated]

Ownership model¶

Failure modes to plan for¶

• Regional dependency degradation while the app tier appears healthy. [Observed]
• Cache or identity provider latency creating user-visible failures before CPU metrics show stress. [Correlated]
• Safe rollback becoming impossible because database changes were not backward-compatible. [Validated]

Trade-offs to keep visible¶

• Higher availability targets usually require stronger dependency governance, not only more instances. [Inferred]
• Multi-region readiness adds release and data complexity that should be justified by business continuity needs. [Correlated]
• Synthetic monitoring is useful only when it reflects real user journeys. [Validated]

Architecture review checklist¶

• Are health checks tied to readiness rather than simple liveness?
• Can deployment rollback happen without database incompatibility?
• Do SLOs and error budgets drive release decisions?

Revisit triggers¶

• Customer-visible incidents occur without being detected first by telemetry. [Observed]
• Regional dependency issues dominate outage minutes. [Observed]
• Release speed is increasing but rollback confidence is not. [Correlated]

Decision takeaway¶

Reliable public workloads combine edge health, safe deployment mechanics, and dependency-aware continuity planning in one operating model. [Validated]

Microsoft Learn references¶

• Azure App Service reliability
• Azure Monitor overview
• Disaster recovery strategies