The estate cloud tools cannot see, and the one that does not exist yet.
DigiUsher normalizes CAPEX amortization, ISV licenses, facilities, mainframe MIPS and SaaS subscriptions into the same FOCUS schema as cloud spend.
That is what makes cloud-versus-data center unit economics honest. And when a new cost domain appears, it lands in the same schema and inherits every platform capability on day one.
Every subscription and on-premise estate lands in the same FOCUS schema, so a SaaS seat, a VMware host and a cloud instance are directly comparable. SaaS is allocated by actual seat activity rather than seat count, which is how shelfware surfaces.
Waste scenarios
Where the money actually goes.
Each scenario carries severity, saving and evidence, and becomes a pull request against the repository that owns the resource, applied only after human approval.
Illustrative. The comparison is only honest when CAPEX amortization,
facilities and operational staff sit in the same schema as cloud line items.
01
CAPEX amortization
Hardware, facilities and support amortized over useful life and attributed to the workloads that consume it, the only basis on which a cloud-versus-data center comparison means anything.
02
Shelfware
Seats purchased against seats actually used, per application and per department. Renewal conversations change when the utilization number is on the table before the vendor arrives.
03
License rationalization
ISV licenses attached to workloads that no longer need them, duplicated entitlements across business units, and premium editions bought for a feature one team uses twice a year.
04
Migration business cases
Cost per VM and cost per MIPS against cost per workload in cloud, computed from the same schema, so the case is arithmetic rather than a vendor spreadsheet.
05
Hybrid allocation
Workloads spanning data center and cloud allocated once, in one chargeback pipeline, rather than reconciled by hand at quarter end.
Signature value metric
Cost per VM · per active seat · per MIPS
Which subscriptions are shelfware; what the data center honestly costs against cloud. Because every domain shares one schema, this metric composes with the others into an AI-inclusive cost per customer: one formula, one auditable lineage.
Compose
Across domains
Cost per customer can include this domain's cost alongside cloud, AI
tokens, data credits, Kubernetes pods and SaaS seats. Point tools each
compute a fraction; one schema computes the whole number.
Verify
Savings that landed
Every optimization moves identified → applied → verified-realized, where
the third state means subsequent billing data confirms the reduction.
Most tools report the first and let you assume the third.
Ask
Conversationally
The full data model is exposed over the Model Context Protocol, so
Claude, Copilot, Gemini or an in-house LLM can answer questions about
this domain under the same role-based access control as the dashboards.
Frequently asked questions
Direct answers on on-premise and SaaS cost allocation.
Can DigiUsher track on-premise and data center cost?
+
Yes. CAPEX amortization, ISV licenses, facilities and mainframe MIPS are normalized into the same FOCUS schema as cloud spend, producing cost per VM and cost per MIPS that are directly comparable with cost per workload in cloud.
How is SaaS cost allocated?
+
By actual seat activity rather than seats purchased. The gap between the two is shelfware, and quantifying it per application and per department is what changes a renewal conversation.
What does ANY mean as a cost domain?
+
That a cost source which does not exist yet lands in the same FOCUS schema through a connector and inherits every platform capability on day one: allocation, unit economics, anomaly detection, forecasting and governed automation. AI arrived exactly this way.
Does adding a new domain require a platform upgrade?
+
No. Because every domain shares one schema and one set of generic capabilities, a new cost source costs a connector rather than a re-platform. That is the structural difference between a value platform and a collection of point tools.