Method & citations

What the app does, where it comes from, what we added — and what it cannot do.

The published method it implements

Cloud Value implements the cloud-adoption suitability and return-on-investment approach set out by Misra & Mondal (2011), as worked through in Lynn, Mooney, Rosati & Fox (2020).

Three elements come directly from that work:

A suitability screen before any financial modelling. Four factors — size of the IT estate, workload variability, data sensitivity and workload criticality — decide whether cloud is a structural fit at all. Organisations clearly unsuited are told so before spending effort on cost modelling.
Total cost of ownership in three categories — upfront, recurring and termination — computed separately for the cloud scenario and the existing on-premise scenario, so the comparison is like for like.
The return-on-investment structure, expressed as ROI = Δ Gross Profit Margin ÷ Cloud TCO, with tangible benefits entering as incremental revenue plus reduced cost.

Scope of the original validation

Stated plainly, because it bounds everything else on this page. The method was demonstrated on one organisation: a roughly 200-person financial services firm operating across EMEA, South America, Asia and Australasia, migrating a foreign-exchange application to Infrastructure-as-a-Service over a five-year horizon.

One worked example is not an empirical benchmark. Our implementation reproduces that published case exactly — cloud total €3,086,188, on-premise total €3,438,652, saving €352,464. That verifies the arithmetic. It says nothing about how well the approach generalises to your business, and we make no claim that it does.

What we added, and why

Each of these is ours, not the cited authors'. They are separated here so you can discount them independently of the published method.

1. A benchmark against not bothering

Published business-case literature compares sophisticated methods against other sophisticated methods. It rarely asks the prior question: does the full exercise beat the quick sum an owner does in their head — comparing running costs and ignoring everything else?

Cloud Value computes both on your figures and reports whether they disagree. When the shortcut reaches the same conclusion, the app says so and tells you that you did not need it. Where the two diverge, it ranks the items responsible so you know exactly what the decision turns on. An app that never admits it added nothing cannot be believed when it claims it did.

2. A compliance and data-residency cost layer

The source method predates the maturing of data-protection and data-sovereignty regimes and treats compliance as a footnote. For many organisations it is now a first-order cost. The app applies an uplift to cloud running costs based on the regulatory position you select.

Defaults draw on published estimates that data-protection governance adds roughly 8–10% to cloud total cost of ownership; that sovereign or in-region cloud carries a 10–30% price premium over standard public cloud; and that regulated sectors report overheads in the 15–27% range. These are industry estimates, not measurements of your business. The app says so at the point of use and tells you to override them whenever you hold a real quote.

The layer is deliberately regime-agnostic — it asks how strong your obligations are, not which country you are in — so the tool works anywhere without encoding one jurisdiction's rules.

3. An input-fitness check before any number is shown

The published method assumed a data-gathering exercise involving ten people across five departments over three months. A single owner-manager at a laptop will not match that, and the tool must not imply equivalent rigour.

Every run is graded on how completely the cost categories were filled in and whether any benefits were entered at all. The grade appears above the result, not beneath it. Blanks are named rather than silently treated as zero, and the app states the direction of the resulting bias: omissions usually favour cloud, because the costs people forget — exit charges, training, compliance — are mostly cloud-side.

4. A downside test

Cloud costs commonly run over early estimates and expected benefits commonly under-deliver. The app re-runs the same case with cloud costs 20% higher and gains 30% lower. Whether the conclusion survives that is a more reliable signal than the headline figure, because it depends less on the accuracy of your estimates — and the app says exactly that.

5. Corrections and clarifications applied

Recurring costs entered per year, not per period. The source case reports multi-year recurring totals, which is easy to misread as annual and understates on-premise cost several-fold. The app asks for yearly figures explicitly and multiplies by the horizon itself.
Revenue discounted to profit. Incremental revenue is not incremental value. New revenue is reduced by your gross margin before entering the calculation.
Payback suppressed when it does not exist. Where the yearly position does not improve there is nothing to pay back from, so no payback figure is shown rather than a misleading one.

What it does not do

Stated here rather than discovered later.

It does not benchmark you against your peers. No credible public dataset of cloud costs and realised savings for organisations of this size appears to exist. Rather than fabricate a comparison, the app offers none — and suggests you ask for the provenance of any tool that does.
It does not price intangible benefits. Agility, resilience, staff skills and credibility with larger customers are real and frequently decisive. They are deliberately excluded from the monetary total, because assigning invented numbers to unmeasurable things is the standard route by which business cases become fiction. Weigh them separately.
It does not discount for the time value of money. No net present value calculation is offered. Over the one-to-five-year horizons this targets, and given the precision of inputs realistically available to a smaller organisation, discounting would add apparent precision without adding accuracy.
It does not model partial or hybrid migration. The comparison is whole-scenario. Moving some workloads and keeping others requires running the tool once per workload.
It does not verify your figures. Everything rests on what you enter. A confident output from poor inputs is the principal risk in any tool of this kind, which is why the fitness grade sits above the answer rather than below it.
It is not a forecast. There is no model and no prediction — the same inputs always give the same output. What the app provides is structure and disclosure, not foresight.

How it runs

Everything executes in your browser. There is no server, no API and no external service. The page declares a Content-Security-Policy of connect-src 'none', so the browser itself blocks any network request the page might attempt. The privacy claim is enforced by the software rather than promised by us, and any future change introducing a network call would fail visibly instead of quietly transmitting your figures.

Charts are hand-drawn SVG rather than a charting library, so there is no external dependency to load, block or version. The source is unminified and readable in your browser's developer tools.

If you use Save on this device, your figures are stored in that browser on that computer only, and Start over erases them. Anyone sharing the browser profile could reload them, so avoid it on a shared machine.

Verification

The implementation is checked against the worked case study published in Lynn et al. (2020, chapter 2). All three reported totals reproduce exactly, along with the derived saving. Boundary behaviour is covered for empty input, single-sided input, zero-benefit runs, and the greenfield path where no on-premise comparator exists.

Available on request for academic review, along with the source.

References

Lynn, T., Mooney, J. G., Rosati, P., & Fox, G. (eds.) (2020). Measuring the Business Value of Cloud Computing. Palgrave Studies in Digital Business & Enabling Technologies. Palgrave Macmillan. Open access. doi:10.1007/978-3-030-43198-3

Misra, S. C., & Mondal, A. (2011). Identification of a company's suitability for the adoption of cloud computing and modelling its corresponding Return on Investment. Mathematical and Computer Modelling, 53(3–4), 504–521.

Rosati, P., & Lynn, T. (2020). Measuring the business value of infrastructure migration to the cloud. In Lynn et al. (eds.), Measuring the Business Value of Cloud Computing, 19–37.

Tallon, P. P., Mooney, J. G., & Duddek, M. (2020). Measuring the business value of IT. In Lynn et al. (eds.), Measuring the Business Value of Cloud Computing, 1–18.

ISACA (2012). Calculating Cloud ROI: From the Customer Perspective. ISACA White Paper.

Citing this tool

If you refer to Cloud Value in academic work:

Quantonique (2026). Cloud Value: browser-based cloud business-case
assessment implementing published suitability and ROI methods with
compliance-cost and input-fitness extensions.
https://cloudvalue.quantonique.com/

Please cite the underlying research above as well — the method is theirs, the implementation and extensions are ours.