# Platform Criteria 5: FinOps Concentration

| **Application** | Visual One Intelligence (VisualOne / VSI) |
| --- | --- |
| **Platform version referenced** | v6.0.0.1 |
| **Criterion** | FinOps Concentration: software functionality concentrating on a specific technology or a specific FinOps activity |
| **Area of concentration** | FinOps for on-premises, private cloud, and hybrid infrastructure, with specialist depth in enterprise storage |
| **Source basis** | VisualOne reference documentation (FinOps for VisualOne Reference Guide; VSI Screen Reference Guide; VSI Virtualization Reporting User Guide; Visual One Storage Reference) |

## Statement of concentration

Visual One Intelligence concentrates on a segment of FinOps that general-purpose cloud cost platforms do not address: **applying rigorous FinOps discipline to infrastructure the organization owns.** Public cloud arrives with a vendor-issued bill; on-premises storage, virtualization, backup, and enterprise compute do not. There is no invoice to parse, no published rate card, and no per-resource charge record, which is why most FinOps tooling stops at the cloud boundary and treats the owned estate as a single opaque line item.

VisualOne's concentration is the construction of that missing cost model from first principles, at device and sub-device granularity, and the application of the full FinOps lifecycle (allocation, chargeback, unit economics, optimization, forecasting, and budgeting) to it. Within that concentration, the platform's deepest specialization is **enterprise storage**, which carries the most detailed screen coverage, the most granular inventory hierarchy, and the most storage-specific optimization logic in the product.

## Depth of specialization: building the cost basis that does not exist

For owned infrastructure, the platform derives a defensible per-resource cost where no billing record exists.

| Element | Implementation |
| --- | --- |
| **Cost component model** | Every device carries configurable Power Cost, Floor Space Cost (inclusive of cooling and power distribution), and Personnel Cost, plus Purchase Price and annual Maintenance Cost |
| **Capital amortization** | Purchase Cost per Day = Total Acquisition Cost ÷ Life of Asset (days), with Life of Asset derived from Purchase Date and Retired Date |
| **All-in daily rate** | Cost of Asset per Day = Daily Operating Cost + Purchase Cost per Day: capital and operating expense resolved into a single daily figure per asset |
| **Normalization** | Total Cost per GiB per Day = Cost of Asset per Day ÷ Usable Capacity, producing a comparable unit rate across assets of different size, age, vendor, and class |
| **Published methodology** | Ten documented sections of formulas with worked examples covering operating cost, normalization, depreciation, TCO, allocation, variance, forecasting, savings realization, payback, and ROI |
| **Administrative source of truth** | An admin-scoped Operating Cost screen holds the per-device assumptions, editable inline or through a CSV export, edit, and re-upload cycle |
| **Worked outcome** | A 100 TiB array at $100,000 over a 1,825-day life with $12.67/day operating cost resolves to $67.46 per day all-in, $0.0659 per GiB per day, and $246.23 per TiB annually |

## Depth of specialization: enterprise storage

| Element | Implementation |
| --- | --- |
| **Screen coverage** | 23 documented storage screens, the largest single module in the platform |
| **Inventory hierarchy** | All Units views at six distinct levels: Devices, Pools, Volumes, LUNs, Shares, and Nodes |
| **Estate segmentation** | Storage Environment views by Data Center, by Classification, and by Geography |
| **Storage-native waste detection** | Orphaned LUNs (allocated but unattached volumes) and Volume Locked Free Space (capacity trapped inside allocated volumes), findings that require storage-layer understanding rather than generic utilization thresholds, valued per array in both dollars and reclaimable GiB |
| **Capacity fidelity** | Effective vs. physical capacity, deduplication and compression status, and Data Reduction Ratio trending, so cost is normalized against genuinely usable capacity |
| **Dual-axis planning** | Pool and Device Capacity Plans model both Capacity (GiB) and Performance (IOPS / bandwidth) constraints, with a scenario dialog accepting date, capacity, workload, growth, and notes, and identifying capacity-exhaustion dates |
| **Storage health specificity** | Alert categories including Storage Layout, Hard Quota Applications, and Advisory Quota; status indicators for Balance, Server Volume Mapping, Storage Layout, and Volumes & Snapshots |
| **Media and topology awareness** | Drives by Type distribution (SSD / SAS / SATA / Fibre), device Tier (0 to 10), Classification (Block / File / Object / Archive), and Physical Diagram topology |
| **Vendor breadth within the specialization** | NetApp, Dell EMC (Isilon, Unity, VPLEX, XtremIO, Compellent), IBM (V7000, FlashSystem, DS8000, SVC), HPE 3PAR, Hitachi (Ops Center, HNAS, VSP), and Pure Storage |

## Depth of specialization: virtualization and private cloud

| Element | Implementation |
| --- | --- |
| **Module scope** | 16 documented screens in the Virtualization Reporting module |
| **Object hierarchy** | Environment / vCenter to Data Center to Cluster to Host to VM to Datastore / LUN to VMDK |
| **Private-cloud-native capacity concepts** | Build Capacity (how many additional VMs fit, and negative when a constraint is exceeded), Weeks Left to Capacity, Capacity Score (0 to 100), and Capacity Constraint / Limiting Factor |
| **Sub-host cost allocation** | CPU Cost % and Memory Cost % (typically 50/50) decompose a shared host into per-vCPU and per-GiB-memory rates, enabling VM-level chargeback of owned hardware |
| **Owned-estate optimization** | VM Right-Sizing with target allocations and an Approve action; Orphaned VMDK inventory sortable by size; CPU and memory over-provisioning valued per vCenter |
| **Hardware scenario modeling** | Cluster Modeling with custom host model definitions and month-to-add, reporting resulting vCPU, memory, disk, VM build, and cost positions to justify a purchase before it is made |

## The complete FinOps lifecycle applied to owned infrastructure

The concentration is not partial coverage of FinOps for infrastructure. It is the full lifecycle.

| FinOps activity | Applied to owned infrastructure |
| --- | --- |
| **Allocation** | Device and host tagging, unified tag keys with Foreign Tag Key mapping, All Tags catalog, Tag Summary and Tag Cost Inventory |
| **Chargeback** | Chargeback Report by any tag dimension with resource-level bill backup, three allocation models (equal, usage-based, tag-based) |
| **Unit economics** | Price per GiB, $/TiB annual and daily, cost per GiB per day, per-vCPU and per-GiB-memory rates, TCO per TiB |
| **Optimization** | Savings Report and Optimization Recommendations spanning storage, compute, and cloud findings in one place, with Open / Suggested / Applied adoption tracking and Realization % measurement |
| **Forecasting** | Tag Forecasting projecting Compute, Storage, Cloud, and Total effective cost past the current date; capacity runway forecasting; four documented forecast methods |
| **Budgeting** | Cost Per Sector budget vs. actual with variance amount and percentage, and a monthly variance review cycle |
| **Asset finance** | Financial Administration Report and Amortization Report delivering depreciation schedules, TCO, and a general-ledger-reconcilable fixed asset position |
| **Efficiency scoring** | Cost Efficiency Dashboard scoring owned assets 0 to 100 and tiering them Optimal / Efficient / Underutilized / Wasteful |
| **Standards interchange** | FOCUS Export expressing on-premises cost in the same open specification as cloud billing data |

## What the concentration produces

| Outcome | Basis |
| --- | --- |
| **A single hybrid cost picture** | Storage, Compute/Virtual, and Cloud normalized into one model, with a Total roll-up, rather than a cloud tool and a separate infrastructure spreadsheet |
| **Chargeback across the whole estate** | One Chargeback Report allocating owned and cloud infrastructure to the same departments on the same basis |
| **Defensible unit rates for owned assets** | Published formulas and administrator-controlled inputs, producing per-unit costs that survive scrutiny from Finance and audit |
| **Waste findings cloud tools cannot see** | Orphaned LUNs, volume locked free space, orphaned VMDKs, and over-provisioned owned hardware, each valued in currency and reclaimable capacity |
| **Capital planning integrated with FinOps** | Depreciation, asset life, refresh timing, TCO comparison, payback, and ROI held in the same platform as operational cost |
| **Allied discipline alignment** | ITAM asset lifecycle, ITFM depreciation and GL reconciliation, and ITSM escalation via ServiceNow, all working from the shared dataset |

## Summary

| Item | Position |
| --- | --- |
| **Area of concentration** | FinOps for on-premises, private cloud, and hybrid infrastructure |
| **Deepest specialization** | Enterprise storage: 23 screens, six-level inventory hierarchy, storage-native waste detection, dual-axis capacity and performance planning, 16 storage vendor platforms |
| **Secondary specialization** | Virtualization and private cloud: 16 screens, full object hierarchy, build capacity and constraint modeling, sub-host cost allocation |
| **Distinguishing capability** | Constructing a defensible per-resource cost basis for infrastructure that issues no bill, using a published and administrator-controlled cost model |
| **Lifecycle completeness** | Allocation, chargeback, unit economics, optimization, forecasting, budgeting, asset finance, efficiency scoring, and FOCUS interchange all applied to owned infrastructure |

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Source: https://visualoneintelligence.com/docs/fof-platform-5-finops-concentration/