Infrastructure Intelligence Advisors
Take Back Control of Your IT Budget.

Cloud. Data center. VMware. Network. AI. Security. Applications. They used to be separate decisions. They aren’t anymore. LogiCloudiQ orchestrates them as one infrastructure strategy—driving down cost while increasing security, governance and control.

Zero cost. No obligation. Bring us the infrastructure decision you’re facing.

$1Msaved on one VMware renewal
100+renewals negotiated
40–60%target infrastructure savings
LogiCloudiQ Intelligent Orchestration LogiCloudiQ INTELLIGENT ORCHESTRATION PUBLIC CLOUD VMWARE / VCF AI + TOKENS NETWORK SECURITY DATA CENTER
Supplier Economics

Your providers optimize their revenue. Who optimizes yours?

The commercial model of each major supplier naturally pushes toward more consumption, longer commitments or broader platform adoption. Click each category to see the financial pressure—and the technical answer that can change the economics.

HYPERSCALERS

AWS · Azure · Google Cloud

More workloads. More consumption. More egress.

See the Grab + Answer
THE FINANCIAL GRAB

Consumption compounds quietly.

  • Compute, storage, managed services and data-transfer charges increase as workloads expand.
  • Discount programs and committed-spend agreements can reward greater consumption while making exit economics harder.
  • Egress and inter-cloud traffic can turn application architecture into a recurring network tax.
  • Convenience services can create a premium that becomes difficult to unwind once applications depend on them.
THE TECHNICAL ANSWER

Change workload placement and traffic economics before negotiating price.

  • Right-size compute and identify idle, oversized or poorly placed workloads.
  • Use private connectivity and NaaS where it can reduce addressable egress without forcing application redesign.
  • Model public cloud, private cloud and selective repatriation by workload—not by vendor preference.
  • Align commitments only after utilization, growth and workload-placement decisions are understood.
Target savings leversCompute rightsizing · Egress reduction · Commitment optimization · Selective repatriation
BROADCOM / VMWARE

VCF · Core Economics · Renewal Leverage

More cores. More VCF. More committed revenue.

See the Grab + Answer
THE FINANCIAL GRAB

The renewal can become a revenue event before it becomes a technical event.

  • Core-based licensing and packaging can increase spend even after infrastructure has been consolidated.
  • Broad platform bundles can cause customers to pay for capabilities they do not fully use.
  • Multi-year terms and renewal timing can reduce commercial leverage as the deadline approaches.
  • Hardware refresh, licensing and migration are often evaluated separately even though they drive the same TCO decision.
THE TECHNICAL ANSWER

Separate the workloads that need VMware from the workloads that simply happen to run there.

  • Right-size licensed cores before the renewal is quoted.
  • Segment workloads by business requirement, portability, performance and migration complexity.
  • Compare VCF, alternate VMware consumption models, private cloud and non-VMware platforms.
  • Model licensing, hardware and migration together so the enterprise can choose the lowest-risk economic path.
Target savings leversCore reduction · SKU rationalization · Alternate consumption · Refresh avoidance · Workload separation
CARRIERS

Network · DIA · Private Access · Contracts

More circuits. Longer contracts. Separate economics.

See the Grab + Answer
THE FINANCIAL GRAB

Static connectivity is often sold into a dynamic workload environment.

  • Fixed-bandwidth circuits can remain in place long after application and traffic patterns change.
  • Separate DIA, MPLS, cloud-connect and access contracts create fragmented spend with limited visibility.
  • Long terms can preserve legacy architecture because changing the network becomes commercially difficult.
  • Cloud egress is frequently treated as a hyperscaler cost even when network architecture can materially affect it.
THE TECHNICAL ANSWER

Turn the network into an economic control point.

  • Inventory circuits, utilization, contract terms and application traffic before renewing.
  • Compare fixed connectivity with NaaS, SD-WAN and private hyperscaler access.
  • Use private transport where it lowers cloud-transfer cost or improves application performance.
  • Retain existing circuits where they remain economically sound and remove or replace only what no longer fits.
Target savings leversContract consolidation · Bandwidth rightsizing · NaaS · Private access · Egress reduction
None of them is responsible for optimizing your total infrastructure economics. We are.
The AI Infrastructure Framework

Four stacks. One economic system.

01 / COMPUTE ECONOMICS

Run tomorrow’s workloads on tomorrow’s economics.

VMware, private cloud, public cloud, GPU, storage, hardware refresh and workload placement.

COST ↓   CONTROL ↑
02 / NETWORK ECONOMICS

Your network may be the fastest path to a lower cloud bill.

NaaS, private connectivity, SD-WAN, hyperscaler egress, inter-cloud and DIA.

COST ↓   PERFORMANCE ↑
03 / AI ECONOMICS

AI consumption is becoming infrastructure consumption.

Tokens, models, AI gateways, RAG, FinOps, application modernization and pooled consumption.

COST ↓   GOVERNANCE ↑
04 / CONTROL

Innovation without governance becomes technical debt.

Identity, SASE/SSE, data security, compliance, CX and policy that follows the workload.

SECURITY ↑   CONTROL ↑
The AI OSI Model

From the power grid to the copilot.

A decision at one layer changes the economics of the layers around it. Click a layer to pressure-test the questions your team should be asking—and the solution paths LogiCloudiQ would evaluate.

01
EnergyData Center Power & Density
Questions + Solutions
QUESTIONS TO PRESSURE TEST
  • Are rack density, power availability and cooling capacity constraining your next refresh?
  • What is the true all-in cost of adding capacity on-prem versus colo or private cloud?
  • Will AI/GPU workloads force electrical, cooling or floor-space changes?
  • Are facilities investments being modeled separately from compute decisions?
LOGICLOUDIQ SOLUTION PATH
  • Model power, cooling, density and facilities cost against workload growth.
  • Compare on-prem refresh, colocation, private cloud and higher-density options.
  • Align AI/GPU requirements to the location that can support them economically.
  • Avoid facilities capex that is only required because workloads were placed incorrectly.
02
ComputeVMware Renewal Economics
Questions + Solutions
QUESTIONS TO PRESSURE TEST
  • What will the next VMware renewal cost after current core counts and packaging are applied?
  • Which workloads truly require VMware and which can move without business disruption?
  • Is a hardware refresh being evaluated independently from licensing economics?
  • What happens to cost if VMware is consumed through a different commercial model?
LOGICLOUDIQ SOLUTION PATH
  • Right-size cores before renewal and separate required VMware workloads from optional ones.
  • Benchmark VCF, alternate VMware delivery, private cloud and hypervisor alternatives.
  • Model licensing, hardware, support and migration as one TCO decision.
  • Create commercial leverage before the renewal becomes a procurement deadline.
03
DataData Protection & Recovery
Questions + Solutions
QUESTIONS TO PRESSURE TEST
  • Are backup, replication, DR and primary storage creating duplicated cost?
  • Do all workloads have recovery policies that match their actual business criticality?
  • Are you paying cloud-retention or egress costs that could be avoided?
  • Can you prove recovery performance against defined RPO and RTO targets?
LOGICLOUDIQ SOLUTION PATH
  • Classify workloads by recovery requirement and business criticality.
  • Remove unnecessary duplication across storage, backup, replication and DR.
  • Evaluate immutable backup, cloud recovery and alternate retention models.
  • Align protection spend to measurable RPO/RTO outcomes rather than blanket policy.
04
NetworkNaaS + Egress Savings
Questions + Solutions
QUESTIONS TO PRESSURE TEST
  • How much of your cloud bill is really a network problem?
  • Are ExpressRoute, Direct Connect, DIA and carrier contracts being evaluated together?
  • Can private access reduce hyperscaler egress without changing the application?
  • Are fixed circuits still appropriate for changing workload patterns?
LOGICLOUDIQ SOLUTION PATH
  • Map application traffic, egress and carrier spend as one transport economy.
  • Compare NaaS, private access, SD-WAN and existing circuits.
  • Use private connectivity where it changes cloud economics without app redesign.
  • Shift static network cost toward more flexible consumption where appropriate.
05
CloudCloud Consumption Optimization
Questions + Solutions
QUESTIONS TO PRESSURE TEST
  • Are workloads in the right cloud—or in cloud at all?
  • Are commitments, compute, storage and egress being optimized together?
  • What workloads are overprovisioned, idle or paying a convenience premium?
  • Are cloud decisions creating downstream network or security cost?
LOGICLOUDIQ SOLUTION PATH
  • Baseline workload utilization and total cloud consumption.
  • Model commitments, placement, rightsizing and egress together.
  • Compare public cloud, private cloud and repatriation where economically justified.
  • Tie workload placement to performance, governance and unit economics.
06
ControlSecurity & Governance
Questions + Solutions
QUESTIONS TO PRESSURE TEST
  • Can identity and policy follow the workload across cloud, SaaS and private infrastructure?
  • Are overlapping security tools solving the same control problem multiple times?
  • Do you know which controls are required by workload versus inherited from vendor architecture?
  • Can governance scale without slowing every new infrastructure decision?
LOGICLOUDIQ SOLUTION PATH
  • Consolidate controls around identity, data, access and policy.
  • Evaluate SASE/SSE, security tooling and governance across the full architecture.
  • Remove overlapping products where common control points can do the job.
  • Design governance that follows the workload instead of each vendor silo.
07
IntelligenceAI Consumption & FinOps
Questions + Solutions
QUESTIONS TO PRESSURE TEST
  • Who is using which models, at what cost, and for what business outcome?
  • Are users buying overlapping AI tools outside a governed enterprise strategy?
  • Can sensitive data reach public models without policy enforcement?
  • How will token, model and API consumption be measured before it scales?
LOGICLOUDIQ SOLUTION PATH
  • Centralize approved model access and policy where appropriate.
  • Create AI FinOps around tokens, APIs, gateways and pooled consumption.
  • Connect identity and data policy to model access.
  • Measure adoption, business value and cost before AI consumption becomes another uncontrolled utility.
08
ExperienceCX Effectiveness & Reporting
Questions + Solutions
QUESTIONS TO PRESSURE TEST
  • Are CX and contact-center investments improving measurable customer outcomes?
  • Can you tie AI automation to containment, handle time, service levels or revenue?
  • Are customer and operational data fragmented across disconnected platforms?
  • Do executives have one view of experience, cost and operational performance?
LOGICLOUDIQ SOLUTION PATH
  • Establish baseline CX and operational KPIs before adding more tooling.
  • Integrate reporting across customer, application and operational systems.
  • Apply AI only where it changes measurable service or cost outcomes.
  • Build one reporting layer that ties infrastructure decisions to business experience.
One infrastructure · Eight interconnected layers · One economic strategy
Start With the Money

Bring us one infrastructure decision. We’ll show you what it changes.

Choose the problem creating pressure. Each decision opens into the financial issue, the technical paths available, and the target financial and technical outcomes we would pursue.

01 / INFRASTRUCTURE DECISION

VMware / VCF

See Problem + Answer
THE FINANCIAL PROBLEM

Renewal pricing can rise even after core reductions, while licensing, hardware refresh and migration are often budgeted separately. Waiting until the quote arrives compresses options and commercial leverage.

TECHNICAL ANSWER POSSIBILITIES
  • Right-size cores and clusters before renewal.
  • Separate workloads that truly require VMware from workloads that can move.
  • Compare VCF, alternate VMware consumption, private cloud and non-VMware platforms.
  • Model licensing, hardware, support and migration as one TCO decision.
TARGET FINANCIAL OUTCOME
30–50%Target VMware/TCO reduction
20–35%Target refresh-capex avoidance
TARGET TECHNICAL OUTCOME
20–40%Target VMware core reduction
0Forced migrations required solely for savings
Discuss This Problem →
02 / INFRASTRUCTURE DECISION

Public Cloud

See Problem + Answer
THE FINANCIAL PROBLEM

Cloud spend compounds across compute, storage, managed services, commitments and egress. Teams often optimize line items independently even though workload placement and network architecture drive the same bill.

TECHNICAL ANSWER POSSIBILITIES
  • Baseline utilization, idle resources and oversized workloads.
  • Model reserved/committed consumption only after rightsizing.
  • Evaluate private connectivity and NaaS where egress economics can change.
  • Compare public cloud, private cloud and selective repatriation by workload.
TARGET FINANCIAL OUTCOME
15–30%Target cloud-consumption optimization
15–35%Target addressable egress savings
TARGET TECHNICAL OUTCOME
20–40%Target compute rightsizing opportunity
1Workload-placement model tied to economics and performance
Discuss This Problem →
03 / INFRASTRUCTURE DECISION

Network

See Problem + Answer
THE FINANCIAL PROBLEM

Fixed circuits, legacy WAN design, cloud-connect products and carrier contracts can continue long after traffic patterns change. Network cost is often disconnected from cloud and application economics.

TECHNICAL ANSWER POSSIBILITIES
  • Inventory circuits, utilization, terms and renewal dates.
  • Compare existing WAN with NaaS, SD-WAN and private hyperscaler access.
  • Use private transport where it improves cloud-transfer economics or performance.
  • Keep existing circuits where they remain technically and financially sound.
TARGET FINANCIAL OUTCOME
20–40%Target WAN/network-cost reduction
15–30%Target contract-consolidation opportunity
TARGET TECHNICAL OUTCOME
30–60%Target provisioning-speed improvement
Carrier and circuit complexity
Discuss This Problem →
04 / INFRASTRUCTURE DECISION

Data Center

See Problem + Answer
THE FINANCIAL PROBLEM

A refresh can trigger simultaneous spend on servers, storage, VMware, power, cooling, space and support. Treating each as a separate project can lock the enterprise into another expensive cycle.

TECHNICAL ANSWER POSSIBILITIES
  • Profile workloads and growth before refreshing hardware.
  • Model power, cooling, density and space requirements.
  • Compare on-prem refresh, colocation, private cloud and public cloud.
  • Place each workload where lifecycle cost and operational requirements align.
TARGET FINANCIAL OUTCOME
20–35%Target infrastructure TCO reduction
20–40%Target refresh-capex avoidance
TARGET TECHNICAL OUTCOME
15–30%Target compute/right-sizing opportunity
1Architecture aligned to future density and workload growth
Discuss This Problem →
05 / INFRASTRUCTURE DECISION

AI

See Problem + Answer
THE FINANCIAL PROBLEM

AI costs can grow through overlapping licenses, token consumption, model APIs, gateways and specialized infrastructure before the enterprise has clear governance or unit economics.

TECHNICAL ANSWER POSSIBILITIES
  • Inventory approved and unapproved AI usage.
  • Centralize model access and policy where appropriate.
  • Create AI FinOps around tokens, APIs, gateways and pooled consumption.
  • Connect identity, data access and business outcomes to model usage.
TARGET FINANCIAL OUTCOME
15–30%Target AI-license consolidation
10–25%Target AI-consumption optimization
TARGET TECHNICAL OUTCOME
100%Approved AI access governed
1Centralized model, policy and usage visibility layer
Discuss This Problem →
06 / INFRASTRUCTURE DECISION

Architecture

See Problem + Answer
THE FINANCIAL PROBLEM

Three-year infrastructure decisions are often made inside separate projects—cloud, VMware, network, security, AI—without modeling how one decision changes the economics of the others.

TECHNICAL ANSWER POSSIBILITIES
  • Map workload dependencies across compute, cloud, network, data and control layers.
  • Model multiple landing zones before procurement.
  • Benchmark provider pricing and commercial structures.
  • Sequence decisions so one contract does not eliminate better downstream options.
TARGET FINANCIAL OUTCOME
20–40%Target cross-stack cost optimization
15–30%Target avoidance of duplicative infrastructure spend
TARGET TECHNICAL OUTCOME
100%Major workload decisions mapped to dependencies
1Integrated architecture and commercial decision model
Discuss This Problem →

Target outcomes are modeled ranges, not guaranteed results. Actual results depend on architecture, contracts, utilization, timing and implementation choices.

Proof, Not Theory

Orchestration changes the economics.

HEALTHCARE INFRASTRUCTURE CASE
$2.075M → $1.111M

Infrastructure spend was reduced by treating the environment as one interconnected economic system—not a series of isolated projects. The strategy evaluated VMware licensing and core optimization, AWS consumption and workload placement, cloud egress, NaaS and private connectivity, data-center and compute economics, and provider commercial terms together—allowing savings in one layer to influence decisions and costs across the others.

46% lower

modeled annual infrastructure spend

Reduced vendor exposure
No forced application migration
Intelligence Before Procurement

We don’t sell the answer before we know the question.

01

Understand

Workloads, dependencies, contracts, utilization and business requirements.

02

Model

Architecture, landing zones, cost, risk, governance and alternatives.

03

Benchmark

Market pricing, provider terms and competitive commercial structures.

04

Decide

The workload determines the answer. Not the vendor.

Representative Use Cases

Infrastructure orchestration in the real world.

Choose a vertical. Its use cases stay hidden until you request them. Then open only the specific problem you want to explore.

Legal

2 Use Cases

Explore Legal infrastructure economics and orchestration.

Explore Legal +
Legal · 01VMware + Data CenterComputeView Use Case +

THE PROBLEM

Technical

VMware supports document management, SQL, VDI and other latency-sensitive applications while core infrastructure is approaching refresh.

Financial

VMware licensing and hardware refresh costs arrive at the same time, creating overlapping capital and operating pressure.

THE ORCHESTRATION PROCESS

Baseline workloads → right-size cores → separate workloads that must remain on VMware from those that do not → compare VCF, alternative VMware delivery models, private cloud and selective migration → model licensing and hardware together.

OUTCOME

Financial

Avoid unnecessary refresh spend and reduce exposure to elevated VMware licensing economics.

Technical

Preserve VMware where operationally appropriate while creating a controlled path for workloads that should move elsewhere.

TARGET OUTCOME

Modeled target ranges for this infrastructure decision pattern. Actual results depend on architecture, contracts, utilization, timing and implementation choices.

Financial
30–50%Target VMware/TCO reduction
20–35%Target refresh-capex avoidance
Technical
20–40%Target VMware core reduction
0Forced application migrations required solely for savings
Discuss This Use Case →
Legal · 02AI Governance + DataIntelligenceView Use Case +

THE PROBLEM

Technical

Attorneys and staff increasingly use public AI tools while privileged and client information resides across Microsoft 365, document management and other repositories.

Financial

Multiple AI subscriptions and uncontrolled consumption grow without centralized visibility into utilization or business value.

THE ORCHESTRATION PROCESS

Identify AI usage → classify accessible data → define approved models and applications → establish identity and policy controls → centralize access where appropriate → measure consumption and adoption.

OUTCOME

Financial

Reduce redundant AI licensing and establish visibility into consumption.

Technical

Improve governance over how enterprise AI interacts with confidential and privileged information.

TARGET OUTCOME

Modeled target ranges for this infrastructure decision pattern. Actual results depend on architecture, contracts, utilization, timing and implementation choices.

Financial
15–30%Target AI-license consolidation
10–25%Target AI-consumption optimization
Technical
100%Approved AI access governed
1Centralized visibility layer for model usage and policy
Discuss This Use Case →
Manufacturing

2 Use Cases

Explore Manufacturing infrastructure economics and orchestration.

Explore Manufacturing +
Manufacturing · 03Cloud + NetworkNetworkView Use Case +

THE PROBLEM

Technical

Plants depend on ERP, MES, IoT and cloud applications while traffic crosses facilities, data centers and hyperscalers over traditional WAN architecture.

Financial

Cloud egress, dedicated connectivity and carrier contracts are often evaluated independently even though they are one transport economy.

THE ORCHESTRATION PROCESS

Map application traffic → measure cloud ingress/egress → inventory carrier spend → identify private-cloud access opportunities → model NaaS and alternative connectivity → retain existing circuits where economically appropriate.

OUTCOME

Financial

Reduce avoidable cloud-data-transfer and network costs without requiring application migration solely for savings.

Technical

Improve cloud connectivity, network flexibility and application performance.

TARGET OUTCOME

Modeled target ranges for this infrastructure decision pattern. Actual results depend on architecture, contracts, utilization, timing and implementation choices.

Financial
20–40%Target network-cost reduction
15–35%Target addressable cloud-transfer savings
Technical
30–60%Target improvement in provisioning speed
0Application redesign required solely to pursue network savings
Discuss This Use Case →
Manufacturing · 04Data Center + ComputeEnergy + ComputeView Use Case +

THE PROBLEM

Technical

A hardware refresh approaches while AI, automation and analytics increase compute and power requirements.

Financial

Servers, storage, VMware, facilities, power and GPU capacity are frequently budgeted as separate projects.

THE ORCHESTRATION PROCESS

Profile workloads → model compute requirements → evaluate power/density constraints → compare refresh, colocation, private cloud and public cloud → place each workload against the appropriate economic model.

OUTCOME

Financial

Avoid overbuying infrastructure and expose the full lifecycle cost of another refresh.

Technical

Create an architecture capable of supporting traditional and higher-density workloads without forcing everything into one platform.

TARGET OUTCOME

Modeled target ranges for this infrastructure decision pattern. Actual results depend on architecture, contracts, utilization, timing and implementation choices.

Financial
20–35%Target infrastructure TCO reduction
20–40%Target refresh-capex avoidance
Technical
15–30%Target compute/right-sizing opportunity
1Architecture aligned to higher-density workload readiness
Discuss This Use Case →
Logistics

2 Use Cases

Explore Logistics infrastructure economics and orchestration.

Explore Logistics +
Logistics · 05Network + CloudNetworkView Use Case +

THE PROBLEM

Technical

Distribution centers, warehouses, offices and mobile operations depend on real-time SaaS, ERP, tracking and cloud services across many locations.

Financial

Fixed-bandwidth circuits and fragmented carrier contracts create costs that do not necessarily align with actual usage.

THE ORCHESTRATION PROCESS

Inventory locations and circuits → establish utilization baseline → identify critical application paths → compare existing WAN with NaaS/SD-WAN/private access → consolidate where technically and commercially appropriate.

OUTCOME

Financial

Shift portions of network spend from static capacity toward more flexible consumption and eliminate unnecessary connectivity.

Technical

Improve visibility, provisioning speed and resilience across distributed locations.

TARGET OUTCOME

Modeled target ranges for this infrastructure decision pattern. Actual results depend on architecture, contracts, utilization, timing and implementation choices.

Financial
20–40%Target WAN-cost reduction
15–30%Target contract-consolidation opportunity
Technical
30–60%Target improvement in provisioning speed
Carrier/network dependency count
Discuss This Use Case →
Logistics · 06CX + IntelligenceExperienceView Use Case +

THE PROBLEM

Technical

Customer service, shipment tracking, contact-center and operational systems generate data across disconnected applications.

Financial

CX and AI investments are often not tied directly to containment, handle time, service levels or customer outcomes.

THE ORCHESTRATION PROCESS

Map customer interactions → integrate operational data → establish baseline KPIs → identify automation opportunities → deploy AI where measurable → report against business outcomes.

OUTCOME

Financial

Tie CX technology investment to measurable operating efficiency rather than feature adoption.

Technical

Create a more integrated customer-service environment with consistent reporting and automation.

TARGET OUTCOME

Modeled target ranges for this infrastructure decision pattern. Actual results depend on architecture, contracts, utilization, timing and implementation choices.

Financial
15–30%Target cost-to-serve improvement
10–25%Target CX-platform rationalization
Technical
15–30%Target handle-time improvement
1Unified operational reporting layer
Discuss This Use Case →
Healthcare

2 Use Cases

Explore Healthcare infrastructure economics and orchestration.

Explore Healthcare +
Healthcare · 07Data + ResilienceDataView Use Case +

THE PROBLEM

Technical

Clinical applications, imaging, EHR and backup environments create large data footprints with demanding availability and recovery requirements.

Financial

Primary storage, backup, replication, DR infrastructure and cloud retention can create overlapping costs.

THE ORCHESTRATION PROCESS

Classify workloads and recovery requirements → analyze storage growth → map backup and replication → validate RPO/RTO → evaluate immutable backup, cloud and DR alternatives → align infrastructure cost to recovery requirements.

OUTCOME

Financial

Reduce unnecessary duplication and align protection spending with actual recovery requirements.

Technical

Improve recoverability, resilience and data protection while maintaining appropriate controls.

TARGET OUTCOME

Modeled target ranges for this infrastructure decision pattern. Actual results depend on architecture, contracts, utilization, timing and implementation choices.

Financial
20–40%Target protection/storage optimization
15–30%Target infrastructure-cost reduction
Technical
20–40%Target duplicated-data-footprint reduction
100%Critical workloads aligned to defined RPO/RTO
Discuss This Use Case →
Healthcare · 08Security + AIControlView Use Case +

THE PROBLEM

Technical

AI adoption creates additional paths for users, applications and models to interact with regulated or sensitive data.

Financial

Adding a separate security product for every AI use case increases tooling and operating complexity without necessarily creating centralized governance.

THE ORCHESTRATION PROCESS

Map AI applications and data flows → establish identity controls → classify sensitive information → define model/data-access policies → consolidate enforcement where possible → monitor usage and exceptions.

OUTCOME

Financial

Reduce unnecessary security-tool proliferation and focus investment on common control points.

Technical

Establish consistent governance over AI access, identity and sensitive data without stopping approved innovation.

TARGET OUTCOME

Modeled target ranges for this infrastructure decision pattern. Actual results depend on architecture, contracts, utilization, timing and implementation choices.

Financial
10–25%Target security-tool rationalization
15–30%Target AI-license optimization
Technical
100%Approved AI access governed
1Centralized policy and usage visibility layer
Discuss This Use Case →

Representative use cases illustrate common infrastructure patterns and decision frameworks. Target outcomes are modeled ranges, not guaranteed results, and depend on each organization’s architecture, contracts, utilization and requirements.

About the Team

Built by operators who understand the economics behind the infrastructure.

LogiCloudiQ brings together enterprise infrastructure strategy, commercial leverage and operating discipline to help IT leaders make better decisions across an increasingly interconnected technology stack.

Mark Wyly, Founder and CEO of LogiCloudiQ
FOUNDER & CEO

Mark Wyly

Mark Wyly is a technology executive with more than 20 years of experience across enterprise infrastructure, cloud, networking and technology services. His career includes leadership roles at Lumen, Windstream and Varnish Software, giving him a front-row view into how provider economics shape enterprise technology decisions. He founded LogiCloudiQ to give IT leaders an independent intelligence and orchestration layer—connecting workload requirements, infrastructure strategy and commercial economics before the enterprise commits.

LinkedIn ↗
Mark Szotkowski, COO and CFO of LogiCloudiQ
COO & CFO

Mark Szotkowski

Mark Szotkowski brings more than two decades of technology leadership spanning strategy, sales, operations, cloud and business transformation. A former RapidScale executive, he led national go-to-market and operational initiatives and later served as Chief Strategy Officer, with responsibility extending into strategic growth, business-system automation and M&A strategy. At LogiCloudiQ, he brings operational and financial discipline to the company’s infrastructure intelligence model—connecting strategy to execution and scalable business outcomes.

LinkedIn ↗
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