When organisations ask how much they could save by optimising their Oracle Cloud Infrastructure spend, they want a number, and the number that comes up most often in our work is an average around forty percent. That figure is real, but it is an average across many engagements rather than a promise for any single one, and understanding where it comes from matters more than the number itself. This article sets out the benchmark savings ranges by category of waste, so you can judge where the savings in your own estate are most likely to be.
It is part of our OCI case studies and benchmarks cluster, and it underpins the cost dimension of cases like our manufacturer cost reduction. The ranges are drawn from delivered optimization work, anonymised by sector.
Where the forty percent comes from
The headline average of around forty percent is not a single lever but the sum of several categories of waste, each contributing a share. The biggest is usually over provisioning: resources sized far larger than the workload needs, often inherited from an on premises world where capacity was bought in big fixed blocks. Right sizing those resources to their actual demand commonly recovers a large fraction of the total saving on its own.
The rest comes from idle and orphaned resources that are running but unused, from workloads that could run on cheaper commitment based pricing but are paying on demand rates, from storage tiered wrongly so that cold data sits on expensive fast storage, and from architectural choices that cost more than necessary. No single category delivers forty percent; the average is what you get when several are addressed together, which is why a thorough optimization beats a single quick fix.
Savings ranges by category
It helps to see the categories separately, because where your estate's waste concentrates determines where your savings will come from. An estate riddled with over provisioning has a different savings profile from one that is well sized but paying on demand for steady workloads that should be on commitments. Knowing your own profile is the first step to a realistic estimate.
| Category of waste | Typical saving when present | How it is recovered |
|---|---|---|
| Over provisioning | Large | Right size compute and database to real load |
| Idle and orphaned resources | Moderate | Identify and remove what is unused |
| On demand vs commitment | Moderate | Move steady workloads to committed pricing |
| Storage tiering | Moderate | Move cold data to cheaper tiers |
| Architectural waste | Variable | Redesign costly patterns |
Why savings vary so widely
The reason the saving is a range rather than a fixed number is that estates start from very different places. An estate that has never been optimised, lifted unchanged from over provisioned hardware and left to grow, has a great deal of waste to recover and can see savings well above the average. An estate that has already been tuned has less to find, and pushing for a large percentage there would mean cutting into capacity the workloads actually need.
This is why a credible optimization begins with measurement rather than a promised percentage. The saving available is whatever the waste in the estate amounts to, and that can only be known by looking. Any provider that promises a specific large percentage before examining the estate is guessing, and the responsible approach is to measure first and commit to the saving the evidence supports.
How the savings are verified
A benchmark saving is only meaningful if it is verified, because a number that cannot be checked is a claim, not a result. Verified saving means the spend before and after is measured on a like for like basis, so that the reduction is demonstrably the result of the optimization rather than of a quieter month or a workload that happened to shrink. This verification is what makes the optimization pricing model honest.
It is also why our optimization work is priced as a percentage of verified savings: if the saving cannot be demonstrated, there is no fee, which aligns our incentive precisely with the client's. That model only works because the saving is measured rigorously, and it is the foundation of our cost optimization service and the governance described in our cost governance solution.
Estimating your own saving
To estimate what your estate might save, look at where its waste is likely to concentrate. If it was lifted from on premises hardware and never resized, expect over provisioning to dominate and the saving to be large. If it has grown organically with little governance, expect idle resources and on demand overspend. If it has already been tuned, expect the remaining saving to be modest and concentrated in specific areas.
The only way to turn that judgement into a real number is an assessment that measures the estate, which is what our optimization engagement begins with. The ranges in this article and the result in our manufacturer case show what is achievable, and the case studies pillar ties the cost story to the wider picture of what real OCI engagements deliver.
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