Cloud economics
Fitzroy helps organizations reduce cloud waste, strengthen backend systems, and prepare for the economic demands of AI-scale infrastructure.
$723.4B
Global public-cloud spend forecast in 2025
Source: Gartner
84%
Organizations naming cloud spend as a top challenge
Source: Flexera
63%
FinOps practices already managing AI spend
Source: FinOps Foundation
Market currents
Cloud spending is expanding rapidly, while AI workloads introduce a more expensive and less forgiving class of infrastructure decisions.
Worldwide public cloud end-user spending (USD)
21.5%
Forecast growth
in 2025
Source: Gartner
(November 2024)
Gartner forecasts 21.5% growth in worldwide public-cloud end-user spending in 2025. AI adoption is accelerating the role of cloud infrastructure in business operations.
Cloud intelligence
Global public-cloud market by service type (2025F)
The public-cloud market is now a major operating layer. SaaS remains the largest segment, while infrastructure and platform services increasingly carry the economic pressure created by AI-heavy workloads.
Source: Gartner 2025 public-cloud segment forecast.
Economics planner
An illustrative scenario, not a forecast or measured client result. Adjust the assumptions to explore the arithmetic.
Research context and calculation methodology →Monthly cloud spend
$500,000
Assumed achievable spend reduction
25%
Modeled impact under your assumptions
$1.5M
Modeled annual gross savings
$1.25M
Modeled first-year value
2.0
Simple payback in months
$4.25M
3-year cumulative value
Assumes immediate savings at constant workload and prices. Excludes added recurring costs, implementation ramp-up, taxes, financing, and discounting. Annual savings = monthly spend × assumed reduction × 12; first-year value subtracts implementation cost; simple payback divides implementation cost by monthly savings. Survey estimates do not validate these inputs.
What the assessment examines
Architecture drag
Over-provisioning, idle services, and fragile backend patterns.
Operating controls
Scaling rules, ownership gaps, and missing cost guardrails.
AI readiness
GPU utilization, scheduling pressure, and data-path bottlenecks.
Approach
1. Chart the system
Map cloud spend, backend architecture, workload behavior, and AI infrastructure dependencies.
2. Identify drag
Find avoidable cost, reliability risks, and operational inefficiencies.
3. Build for control
Prioritize the improvements that create measurable value and a more scalable technical foundation.
Cloud expense & reliability diagnostic
What drives spend, and which changes can reduce it without creating production risk?
Typically two working weeks; fee, access, deliverables, and acceptance criteria agreed in the statement of work.
Review scope and deliverables →
Start with an enterprise assessment of your cloud economics, backend architecture, and AI infrastructure readiness. Engagements begin at $7,500.
Discuss a project