Domain 3: Optimize Usage & Cost
| Application | Visual One Intelligence (VisualOne / VSI) |
| Platform version referenced | v6.0.0.1 |
| FinOps Framework domain | Optimize Usage & Cost |
| Capabilities in this section | Architecting & Workload Placement · Usage Optimization · Licensing & SaaS · Rate Optimization · Sustainability |
| Source basis | VisualOne reference documentation (FinOps for VisualOne Reference Guide; VSI Screen Reference Guide; VSI Virtualization Reporting User Guide; Visual One Storage Reference) |
3.1 Architecting & Workload Placement
Capability: Design and modernization of solutions with cost-awareness and efficiency, to maximize business value while meeting performance, scalability, and operational objectives.
How VisualOne enables this
VisualOne supports placement and design decisions by making the cost and constraint consequences of a placement visible before it is made, and by exposing the limiting factor that will bind first.
Supporting features
| Element | Implementation |
|---|---|
| Cost comparison across placement targets | Tag Cost Modeling accepts the same workload definition against Storage, Compute, or Cloud contexts and returns Daily / Monthly / Yearly cost for each, allowing a proposed workload to be priced in alternative placements before commitment |
| Migration modeling | The platform supports migrations and cross-platform cost comparisons; the Virtual module includes Cluster Plans Summary and Cluster Migration Modeling (where enabled) |
| Constraint-aware design | Capacity Constraint / Limiting Factor identifies the resource that limits growth first (frequently memory); Build Capacity states how many additional VMs a target can absorb and may be negative when a constraint is already exceeded; Weeks Left to Capacity gives the runway on that target |
| Placement scoring | Cost Efficiency Dashboard rates every candidate host and array 0-100 and tiers it Optimal / Efficient / Underutilized / Wasteful, so placement can favour assets with headroom and better cost-to-capacity characteristics |
| Host and datastore rebalancing | ESX Host Summary exposes host profile, limiting factor, utilization gauges, build capacity, and the resident VM list, supporting identification of candidates for migration off an overloaded host. VM Datastore Mapping and All Units – Datastores identify hot-spot and underused datastores to drive rebalancing (move VMs, adjust storage policies), validated against the next collection snapshot |
| Storage tier / media awareness | Drives by Type distribution (SSD / SAS / SATA / Fibre) indicates performance characteristics and technology-refresh opportunities; device Tier (0-10) and Classification (Block / File / Object / Archive) support service-level and cost-tier matching when placing workloads |
| Design-stage TCO | Normalized TCO per TiB enables architecture options to be compared on total lifetime economics rather than purchase price, the documented Asset A vs. Asset B example shows the higher-purchase-price option winning on TCO per TiB |
| Hardware add modeling | Cluster Modeling accepts a custom host model and a month to add resource, then reports the resulting vCPU / memory / disk / VM build / cost position, modeling the design change before purchase |
| Physical topology | The Storage module’s Physical Diagram and Storage by Server by Type screens document the existing physical arrangement that placement decisions operate within |
| Post-change validation | Placement and rebalancing outcomes are confirmed at the next collection through Delta Report, VM Summary, and Cluster Trends |
Personas served
Engineering and Architects (placement target selection, constraint analysis, rebalancing), FinOps Practitioner (cost of the placement decision), Product (cost implications of a design choice).
3.2 Usage Optimization
Capability: Analyze and optimize resources to match specific usage patterns, ensuring workloads operate efficiently and generate sufficient business value for their cost.
How VisualOne enables this
VisualOne runs multiple independent optimization engines across storage, compute, virtualization, and cloud, quantifies each finding in dollars, and carries findings through an actionable status workflow.
Supporting features
| Optimization surface | Implementation |
|---|---|
| Savings Report | Context-switched across Total / Storage / Compute / Cloud with a $ / GiB unit toggle in the Storage context so savings can be read as dollars or reclaimable capacity. Observed totals: Cloud $21,516.00, Compute $24,918.00, Storage $833.69 |
| Storage waste detection | Orphaned LUNs (volumes allocated but not attached to any server, root cause: forgotten allocations, decommissioned applications) and Volume Locked Free Space (free capacity trapped inside allocated volumes, root cause: conservative allocation). Grid reports both per array across 16 devices |
| Compute waste detection | Per-vCenter stacked breakdown of Estimated CPU Savings, Estimated Memory Savings, and Orphaned VMDK Cost (observed: Orphaned VMDKs $23,424.00, Memory $1,267.00, CPU $227.00). Typical reductions: CPU 10-30%, Memory 10-25% |
| Orphaned VMDK inventory | Dedicated screen listing VMDK files not attached to active VMs, with vCenter, datastore, VM name, VMDK identifier, VMDK size, acknowledgement, and ticket integration fields. Workflow: sort by size to prioritize high-impact cleanups, export for validation, and validate against change records and retention policies before deletion |
| VM Right-Sizing | Recommendations derived from utilization patterns, filterable by VM name, status, type (CPU / memory / disk), and last updated. Each recommendation carries target allocations and an Approve action, supporting batch approval. Workflow: filter by type (memory first in many environments), approve in batches during a change window, validate in VM Summary after the next collection |
| Utilization categorization | All Units – Hosts categorizes CPU/memory utilization; All Units – VMs applies utilization categories with a cost distribution donut; All Units – Datastores shows utilization distribution and VM density |
| Efficiency scoring | Cost Efficiency Dashboard identifies the specific assets returning least value per dollar (observed Low Value set: NetApp cluster 0.09/100, Isilon 0.31/100, ClearbkupSAN 0.65/100, V7000 0.77/100, Unity 1.07/100) |
| Storage reclamation analysis | Free Space by Disk Group and Provisioning Report support reclaim and over-provisioning analysis; capacity metrics carry effective vs. physical with deduplication/compression status and Data Reduction Ratio trending |
| Recommendation lifecycle | Optimization Recommendations tracks a three-state lifecycle per recommendation, Open / Suggested / Applied, with a per-recommendation count (e.g. “0 Open, 5 Suggested, 0 Applied”), making adoption trackable rather than informational only |
| Savings realization measurement | Actual Savings = Cost Before − Cost After; Realization % = Actual ÷ Projected × 100% (worked example: $40,000 actual against $50,000 projected = 80%). Workflow: identify → approve → implement → track before/after → measure actual vs. projected → document lessons → feed future planning |
| Trend of the opportunity pool | Monthly Estimated Savings chart shows whether the waste pool is growing or shrinking over time |
| Prioritization method | Rank recommendations by savings amount, evaluate implementation effort vs. savings, select high-impact/low-effort quick wins first, schedule, then track realization |
Personas served
Engineering (right-sizing execution, orphan cleanup), FinOps Practitioner (opportunity prioritization and realization tracking), Finance (quantified savings for the plan), Leadership (total opportunity and adoption rate).
3.3 Licensing & SaaS
Capability: Optimize the impact of software licenses and SaaS investments, understanding vendor licensing terms, use rights, and pricing options, and planning to minimize over-deployment (compliance risk) and under-deployment (shelfware/waste).
How VisualOne enables this
VisualOne brings software maintenance, support contract, and subscription cost into the same asset ledger and cost model as hardware, and provides the consumption denominators that per-core, per-socket, and per-VM licensing models are calculated against.
Supporting features
| Element | Implementation |
|---|---|
| Software maintenance and support cost tracking | Maintenance Cost ($/yr) is a first-class, editable field per storage device and per compute host in Operating Cost settings, and Total Maintenance appears in the Financial Administration Report as an annual support cost line covering vendor support contracts, maintenance and patching, and professional services allocation. Observed values include $9,100/yr, $3,000/yr, $2,250/yr |
| Subscription-style asset treatment | The Amortization Report distinguishes depreciating hardware from non-depreciating recurring cost, one observed device carries a large flat Effective Cost across all snapshots, unlike depreciating array costs, consistent with an appliance license or subscription rather than a depreciating hardware asset |
| Cloud subscription cost management | Cloud costs are tracked and optimized at the subscription level, with per-subscription estimated savings (observed across four Azure subscriptions: $13,542, $4,444, $2,891, $639) and subscription identity carried as Sub Account ID/Name/Type in the FOCUS Export |
| Licensed-software estate visibility | Backup and data-protection software platforms are ingested as first-class collector types, VEEAM_BACKUP, COMMVAULT_BACKUP, COHESITY_BACKUP / COHESITY_HELIOS_DEVICE / COHESITY_HELIOS_SAAS_DEVICE, AVAMAR_DEVICE, DATA_DOMAIN_DEVICE, HP_STOREONCE_DEVICE, giving visibility into the environments those licenses cover, plus a dedicated Backup reporting section |
| Licensing cost avoidance through right-sizing | VM Right-Sizing is documented as being used to lower licensing and infrastructure costs and to standardize VM sizing and prevent sprawl, directly relevant to per-core, per-socket, and per-VM licensing models |
| Consumption basis for license planning | Total Cores and Total Memory per host, cluster membership, vCenter association, host model, serial number, and VM counts are all maintained, the standard denominators for hypervisor and per-core license entitlement calculations |
| Contract cost in TCO | Annual maintenance and support cost flows into Annual Operating Cost, Cost of Asset per Day, and TCO, so licensing and support are reflected in normalized per-unit cost rather than sitting outside the model |
| Third-party FinOps tool integration | CLOUDABILITY and RIGHT_SIZE collector types allow license and SaaS-relevant data held in adjacent tooling to be brought into the same dataset |
Personas served
Finance (maintenance and subscription cost in the asset ledger), Engineering (sizing decisions that drive license consumption), FinOps Practitioner (subscription-level cloud spend), Procurement (support contract cost by vendor and asset).
3.4 Rate Optimization
Capability: Manage resource rate efficiency through commitment discounts (RIs, Savings Plans, Committed Use Discounts) and other pricing mechanisms, to meet operational and budgetary objectives.
How VisualOne enables this
VisualOne surfaces commitment-discount opportunities as discrete, quantified, status-tracked recommendations drawn from multiple underlying optimization engines.
Supporting features
| Element | Implementation |
|---|---|
| Commitment recommendations | Optimization Recommendations grid carries recommendation text, Estimated Savings, Recommendation Count, and Status(es). Observed rows: “Consider purchasing a savings plan to unlock lower prices”, $20,275.00, count 5; “Consider purchasing a savings plan for compute to unlock lower prices”, $1,089.00, count 1; “Consider virtual machine reserved instance to save over the on-demand costs”, $152.00, count 2; “Enable Vertical Pod Autoscaler recommendation mode to rightsize resource requests and limits”, count 1 |
| Recommendation taxonomy with expected yield | Reserved Instance, multi-year commitments for consistent long-term workloads, typical 20-40% reduction, effort = planning and commitment decision. Savings Plan, flexible spending commitments for variable workloads, typical 15-25% reduction, less rigid than RIs. Right-Sizing, typical 10-30%. Orphaned Cleanup, variable |
| Multi-engine sourcing | Recommendation types span cloud savings plans, compute savings plans, VM reserved instances, and Kubernetes vertical pod autoscaling, drawing on multiple underlying cost-optimization engines (cloud provider APIs, VM right-sizing, K8s recommender) |
| Cloud context savings | Savings Report’s Cloud context aggregates commitment-driven opportunity per subscription and typically includes reserved instances and commitment discounts |
| Commitment adoption tracking | The Open / Suggested / Applied status lifecycle per recommendation type, with counts, enables adoption to be tracked rather than only opportunity |
| Opportunity trending | Estimated Savings (Monthly) chart trends the commitment opportunity pool over time (observed Dec 2025 – Jul 2026), showing whether uncommitted spend is rising or being absorbed |
| Portfolio prioritization | Recommendations Distribution donut sizes each recommendation type by savings amount rather than count, so the highest-value rate action is visually dominant. Documented example breakdown: Reserved Instance 79% of a ~$77,000 total opportunity, Right-sizing 18%, Savings Plans 2%, Orphaned Cleanup <1% |
| Commitment business case | Payback Period and ROI formulas support the financial justification of a multi-year commitment; TCO comparison supports evaluating commitment vs. purchase alternatives |
| On-premises rate equivalent | For owned infrastructure, the Financial Administration Report and Amortization Report expose Purchase Cost per Day, asset life, and depreciation schedule, allowing refresh timing and asset-life assumptions to be tuned for the best effective rate per unit of capacity |
Personas served
FinOps Practitioner (commitment strategy and coverage), Finance (commitment business case, multi-year budget impact), Leadership (commitment approval decisions), Engineering (Kubernetes and VM rate mechanisms).
3.5 Sustainability
Capability: Incorporate sustainability criteria and metrics into resource optimization, so environmental efficiency is balanced with FinOps practices.
How VisualOne enables this
VisualOne tracks the physical-resource inputs that drive environmental impact, energy consumption, facility and cooling footprint, and hardware lifecycle, at the individual device level, and every optimization surface in the platform reduces the quantity of powered, cooled, floor-consuming hardware the organization operates.
Supporting features
| Element | Implementation |
|---|---|
| Energy consumption tracked per device | Power Cost ($/month) is a first-class configurable field for every storage device and compute host, representing energy consumption for device operation, calculated from device power specifications and utility rates, and varying by device model and utilization. Also surfaced as Power Cost per Day in the Financial Administration Report. Observed values: $1,366/mo, $455/mo (uniform across nine ESX hosts), $58/mo |
| Facility and cooling footprint | Floor Space Cost ($/month, and per day) explicitly includes cooling and power distribution infrastructure and is based on device footprint and facility costs, a measure of data-center environmental load |
| Waste elimination reduces physical load | Every optimization surface reduces powered hardware demand: Orphaned LUNs, Volume Locked Free Space, Orphaned VMDKs, over-provisioned CPU and memory, and the recommendation to consolidate and retire underutilized hosts |
| Efficiency scoring identifies environmental waste | The Cost Efficiency Dashboard’s Wasteful tier identifies assets consuming power, cooling, and floor space while returning minimal utilization value, in the observed environment, 12 of 14 assets |
| Hardware lifecycle and refresh management | Purchase Date, Retired Date, Array Life (typical 3-5 years), asset status (Active/Retired), and forward depreciation curves support planned refresh cycles and proactive decommissioning rather than run-to-failure |
| Media technology refresh signal | Drives by Type distribution (SSD / SAS / SATA / Fibre) indicates performance characteristics and upgrade opportunities, the input to a refresh toward more energy-efficient flash media |
| Data reduction efficiency | Deduplication and compression status, Data Reduction Ratio trending, and effective-vs-physical capacity reporting quantify how much logical data is served per physical, and therefore powered, TiB |
| Consolidation evidence base | Utilization %, VM density, host and datastore utilization categorization, and build capacity provide the analytical basis for consolidation onto fewer physical systems |
| Location dimension | Data Center and Geography tags allow power and floor-space cost to be analyzed by facility and region |
| Lifecycle disposal | Disposal Cost is carried as a component of the TCO formula, keeping end-of-life handling inside the total cost picture |
Personas served
Engineering (consolidation and refresh decisions), Finance (energy and facility cost lines), FinOps Practitioner (waste reduction with an environmental co-benefit), Allied Sustainability personas (device-level power and facility footprint data).
Section summary
| Capability | Primary supporting surfaces |
|---|---|
| Architecting & Workload Placement | Multi-context cost modeling, cluster and migration modeling, limiting factor and build capacity, efficiency scoring, host/datastore rebalancing, TCO-based option comparison |
| Usage Optimization | Savings Report across four contexts with $/GiB toggle, Orphaned LUN and VMDK detection, CPU/memory savings, VM Right-Sizing with Approve, Open/Suggested/Applied lifecycle, Realization % tracking |
| Licensing & SaaS | Maintenance and support cost per asset, cloud subscription cost and savings, licensed-platform inventory, right-sizing for license cost avoidance, core and memory consumption denominators |
| Rate Optimization | Quantified RI / Savings Plan / VPA recommendations with counts and status, 20-40% and 15-25% expected yields, savings-weighted prioritization, opportunity trending, commitment business case |
| Sustainability | Power and floor-space (including cooling) cost per device, hardware lifecycle and refresh planning, data reduction efficiency, consolidation and waste analysis, location-based footprint analysis |