Azure cost optimisation gets treated as a specialist discipline requiring dedicated FinOps tooling, and for large, complex estates it genuinely is. For most mid-market environments, a handful of checks capture the majority of realistic savings without needing a dedicated cost-management practice.
Right-sizing underutilised virtual machines is usually the first and highest-value check: identifying VMs with consistently low CPU and memory utilisation over a meaningful window — two to four weeks, not a single quiet day — and downsizing where there's genuine headroom. This alone commonly recovers a noticeable share of compute spend with minimal risk.
Automated shutdown scheduling for non-production environments is the second, and often the easiest to implement: development, test and staging environments that run 24/7 out of habit, when the actual usage pattern is business hours only, represent pure waste the rest of the time. Scheduling this automatically, rather than relying on someone remembering to switch things off, is a genuinely low-effort, high-return change.
Reserved capacity and savings plans matter for stable, predictable baseline workloads — committing to a one- or three-year term in exchange for a substantial discount over pay-as-you-go pricing — but only once the workload itself is confirmed stable. Committing capacity for something that's about to be re-architected or retired locks in savings on spend you were going to eliminate anyway.
The last, most overlooked check is simply having budgets and anomaly alerts configured at all. A cost spike from a misconfiguration or a forgotten resource is cheap to fix within a day of it happening and expensive to discover at the end of a billing cycle a month later — the gap between those two outcomes is entirely a matter of whether anyone was watching.
All technical perspectives