How we found ₹1,16,387/month in real Azure waste — traced to the rupee
Most cloud cost tools will tell you that you're "typically" wasting 30–40% of your bill. That number comes from an industry benchmark, not your account. We wanted to know what a real customer's real bill actually showed — so we ran CloudOptimizer AI against a live, production Azure subscription with a real, invoiced monthly bill, and only counted what the data actually supported.
That bill figure isn't an estimate — it's the subscription's actual last-month invoice, pulled directly from Azure's own Cost Management API, the same number that shows up in the Azure portal's billing page. Every recommendation CloudOptimizer AI produced against that account was priced against one of two things: the resource's own real observed cost in that same billing data, or Azure's live retail pricing API when no billing history existed yet for a given resource. Nothing was estimated from a lookup table.
What it actually found
The scan surfaced waste across a handful of familiar categories — none of it exotic, all of it the kind of thing that accumulates quietly in any account that's been running for a while:
| Category | What was happening | Basis for the number |
|---|---|---|
| Dev/staging VMs & AKS clusters | Running 24/7 despite only being used during business hours | Real cost × exact off-hours fraction of the recommended schedule |
| Unattached managed disks | Leftover from deleted or resized VMs, still billing monthly | Real observed cost — deleting removes it entirely |
| Public IPs / load balancers | Reserved but not attached to anything | Real observed cost or live retail price for that SKU |
| Premium-tier disks on low-utilization workloads | Provisioned above what the workload's actual IOPS/throughput needed | 14-day real Azure Monitor utilization data |
Why the number is a range, not a single figure
Not every recommendation had enough real data behind it to justify one exact number. When a resource's real cost existed but the correct percentage to apply wasn't precisely computable — no exact schedule, no direct utilization signal — we showed a labeled low–high range instead of collapsing it into a single point figure that would have been more precise-looking than it actually was.
And where neither a real cost nor a computable real price existed at all — a handful of advisory-only findings, like "this cluster should be tagged with an environment so we can classify it automatically" — we showed no ₹ figure whatsoever. Not a small "~estimated" number next to it. Nothing. If the data doesn't support a figure, the honest answer is no figure, not a smaller guess.
The range itself
Across repeated scans against the same account (re-run as new schedules were approved and some recommendations were acted on), identified monthly savings landed between roughly ₹14,000 and ₹36,000 — a real spread, not noise, because the number legitimately changes as recommendations get approved, disks get resized, and the account's actual usage shifts month to month. A tool that always reports the same tidy percentage regardless of what changed in the account isn't measuring anything real.
That's the whole point of building it this way: a savings number a customer can hold up against their own Azure portal and have it match, rather than a number that just sounds plausible.
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