Episode 56 — Using Azure Advisor for Optimization
Welcome to Episode fifty-six, Using Azure Advisor for Optimization, where we focus on turning guided recommendations into steady, measurable improvements. Azure Advisor is a built-in service that scans your environment and explains where you can save money, reduce risk, and improve operations. It matters because most cloud waste and configuration drift come from small oversights rather than dramatic failures. Advisor highlights those gaps and ranks them, so you can act with confidence instead of guessing. Imagine a dashboard that points directly to oversized virtual machines, open endpoints, or underused databases and explains exactly why each change helps. The service reduces analysis friction by translating platform signals into plain guidance. Used well, it becomes a rhythm: review, act, verify, and record what you changed for next time.
Beyond those core areas, Advisor also surfaces performance and operational excellence insights that improve day-to-day experience. Performance guidance flags noisy neighbors, storage throttling, or query patterns that push latency higher than needed. Operational excellence speaks to maintainability, recommending autoscale settings, consistent monitoring, and configuration health. The practical value is momentum: small, specific fixes compound into more predictable systems. Picture enabling autoscale on a web tier, which flattens response times and reduces weekend paging. Or shifting a database to a tier that matches its real workload rather than its peak from six months ago. These adjustments are modest, but they add up to smoother weeks.
Prioritizing recommendations by impact and effort keeps optimization realistic and repeatable. Impact measures how much the change could save or protect, while effort reflects time, risk, and coordination. A helpful pattern is to label items as quick wins, medium lifts, and strategic refactors, then schedule accordingly. Quick wins might include enabling soft delete on storage or deleting unused snapshots. Medium lifts could resize virtual machines or consolidate disks. Strategic refactors might adopt availability zones or restructure network paths. This framing prevents teams from chasing tiny savings while ignoring high-value work. It also helps leaders allocate the right people to the right tasks.
Automation scripts convert guidance into consistent action. For many recommendations, you can attach a script or runbook that applies the fix the same way every time. This makes changes auditable and reduces human error during busy periods. For example, a rightsizing script can evaluate performance counters, confirm thresholds, then adjust virtual machine sizes during a maintenance window. A cleanup script can find unattached network interfaces and disks, confirm ownership tags, and remove them safely. Over time, these scripts form a library tied to Advisor categories, so a recommendation is not just a suggestion but a repeatable procedure. Automation turns advice into outcomes.
Pairing Advisor with budgets and policies creates a guardrail system that prevents regressions. Budgets and alerts warn when cost trends deviate, catching issues that Advisor may surface later as findings. Azure Policy enforces preventive rules, such as disallowing untagged resources or blocking public endpoints, so fewer problems reach production. Together they close the loop: policies reduce new drift, budgets catch emerging trends, and Advisor suggests concrete fixes for what remains. A practical rhythm is to review budgets weekly, Advisor biweekly, and policy compliance monthly. The cadence keeps financial and technical signals aligned.
Reviewing reservations and Savings Plans alongside rightsizing opportunities produces compounding savings. Rightsizing reduces waste by picking the correct size, while reservations and Savings Plans discount predictable baseline usage. Start by establishing the right size based on recent, representative performance data, then commit to a term for that steady footprint. This avoids locking in overspending and makes discounts more accurate. The same logic applies to databases and analytics services with provisioned capacity. When rightsizing and commitments work together, you gain both correctness and predictability, which finance teams appreciate and engineers feel in quieter alert channels.
Cleaning idle resources and orphaned disks is a reliable, low-risk way to recover costs. Idle compute, unattached disks, stale snapshots, and unused public IP addresses often hide in large estates. Advisor flags many of these, but you strengthen the process with tagging rules that require owners and lifetimes. A small scenario illustrates the point: a development project ends, but its premium disks remain. A monthly cleanup job finds untagged or unattached disks over a certain age, emails the listed owners, and removes them after a grace period. The action is simple, the savings are steady, and the habit prevents slow leakages that add up.
Aligning recommendations to landing zones ensures optimization never breaks design principles. Landing zones define network boundaries, identity policies, and resource hierarchies. When a recommendation suggests a change, you validate it against those guardrails before applying. For example, a cost suggestion to consolidate resources must still respect network segmentation and region strategy. This step keeps local improvements from undermining global architecture. It also accelerates approvals because reviewers can compare changes to documented patterns rather than debating preferences. Alignment preserves coherence as you tune, so the environment stays both efficient and sound.
Tag improvements to clarify ownership and make progress visible. When you implement a fix, update or add tags that reflect the responsible team, the change date, and any renewal window, such as a reservation term end. These tags feed reports that show which groups drive the most savings and which areas need coaching. They also help future maintainers understand why a resource looks the way it does. A tag like “optimized: true; optimizedBy: platform; optimizedOn: twenty twenty-five dash ten dash eighteen” turns invisible work into discoverable history. Ownership tags convert abstract accountability into something the platform can query.
Measuring results and iterating continuously keeps motivation high and guides future efforts. Track a few simple metrics: monthly cost avoided, number of findings resolved, time to remediate, and count of suppressed items with justification. Correlate technical changes with outcome signals such as reduced paging, lower error rates, or faster deployments. Share a brief monthly summary so leaders and engineers see the connection between work and benefit. When a change does not pay off, note it and adjust the selection criteria. Iteration matters as much as action; it teaches the organization which levers truly move outcomes.
A culture of ongoing optimization treats Advisor as a partner rather than a one-time audit. The service surfaces opportunities; your process ranks them, applies automation, and proves the result with budgets and telemetry. Over time, the backlog shrinks, new drift slows, and confidence grows because every decision is visible and repeatable. In that culture, teams do not wait for renewal season or a surprise bill to ask hard questions. They adjust continuously, keep records tidy, and align improvements with landing zones and governance. The result is not only lower spend, but steadier systems and happier teams who know exactly why each change was made.
