An Azure Cost and Performance Review Checklist for .NET Applications

1. Establish the workload baseline

Start with at least one representative business cycle. Separate steady traffic, scheduled processing, month-end peaks, releases, and incidents. Map Azure spend to environments and capabilities rather than reading only the subscription total.

Pair cost with p50 and p95 response time, throughput, error rate, saturation, dependency latency, deployment frequency, and availability targets. Add a unit-cost measure such as cost per active user, processed loan, order, API call, or background job.

• Tag resources consistently by workload, environment, owner, and cost center
• Identify idle resources, unattached storage, duplicate environments, and excessive retention
• Separate fixed baseline cost from demand-driven cost
• Record peak and off-peak utilization before changing tiers
• Create budgets and alerts for unusual spend

2. Review App Service as a shared capacity decision

The App Service plan defines the compute resources shared by its applications. Review CPU, memory, request queue, response time, instance count, and application startup behavior together. One noisy application can make every app on the plan appear under-sized.

Right-sizing is more than moving down a tier. Predictable production workloads may benefit from reservations, while nonproduction environments may use lower tiers or scheduled availability. Scale rules should include sufficient headroom and account for startup time so cost savings do not create a latency incident.

• Enable Always On where unloading would create unacceptable cold starts
• Move CPU-heavy or long-running work away from the request path
• Cache stable expensive results to reduce repeated database work
• Load-test scale-out and scale-in behavior before relying on autoscale
• Use deployment slots with a clear warm-up and rollback strategy

3. Match Azure Functions hosting to execution behavior

Functions are cost-effective when work is event-driven and the hosting plan matches latency and networking requirements. Review invocation frequency, execution duration, memory, concurrency, retries, downstream throttling, and cold-start tolerance.

A function that repeatedly retries a non-idempotent operation can increase cost and corrupt business state. Queue-triggered workloads need poison-message handling, bounded retries, observability, and a concurrency setting that downstream systems can sustain.

4. Treat Azure SQL spend as an application concern

Before increasing database capacity, use Query Store and resource metrics to identify the queries, waits, plans, and workload periods consuming CPU, I/O, memory, log throughput, or worker threads. Application round trips and over-fetching can be as expensive as a poor query plan.

Choose provisioned, serverless, elastic pool, or hyperscale options around workload shape and recovery requirements. A lower compute tier can cost more operationally if it causes timeouts, retries, or manual intervention. Conversely, optimizing a handful of high-impact queries may create enough headroom to avoid a scale-up.

5. Find hidden observability and data-transfer costs

Logs, metrics, traces, backups, replication, and egress are valuable but need explicit retention and sampling policies. Collect the telemetry required to operate the workload, then route high-volume low-value data differently from security, audit, and business-critical signals.

Review cross-region calls, repeated payload transfer, oversized API responses, and chatty service boundaries. These patterns increase both latency and cost. Architecture changes that reduce calls often improve reliability at the same time.

6. Prioritize changes through controlled experiments

Rank findings by expected impact, implementation effort, risk, and confidence. Begin with reversible changes: unused resources, retention, obvious query regressions, schedules, caching, and scale limits. Test each change against the same workload baseline.

Cost optimization is continuous. Add unit cost and performance indicators to operational reviews, and re-evaluate after traffic changes, new features, model usage, data growth, or architectural migrations.