AI Infrastructure Economics & Deployment Diligence Framework

AI Cost per Successful Outcome

Total AI Operating Cost ÷ Successful Outcomes

The AI capability costs $2.00 in recurring operating expense for each successful business outcome produced.

Total AI Operating Cost represents the recurring cost required to operate the AI capability during the measurement period.

• ·Model inference
• ·GPU or cloud compute
• ·Retrieval and embeddings
• ·Storage
• ·Networking
• ·Supporting platform services
• ·Observability
• ·Recurring AI platform costs
• ·Relevant production operations

This metric measures operational unit economics. It answers: 'How much does the AI system cost to produce one successful business outcome?'

Economic Contribution per Outcome

Economic Value per Successful Outcome − AI Cost per Successful Outcome

Each successful outcome contributes $6.00 of economic value after the AI operating cost required to produce it.

Economic Value per Successful Outcome represents the measurable financial value created or preserved by that outcome, such as labor savings, avoided operating cost, incremental margin, additional revenue, increased throughput, or another attributable economic benefit.

This metric answers: 'How much economic value remains after the AI operating cost required to produce the outcome?'

AI ROI

(Economic Benefit − Total AI Cost) ÷ Total AI Cost

The AI investment generated net economic benefit equal to 300% of the total investment required to create and operate it.

Economic Benefit and Total AI Cost must use the same measurement period.

Total AI Cost represents the total economic investment required to create and operate the AI capability over the ROI measurement period.

• ·Initial implementation
• ·Engineering and integration
• ·Model and inference costs
• ·Infrastructure
• ·AI platforms and software
• ·Production operations
• ·Relevant capital expenditure
• ·Financing or cost of capital where material
• ·Maintenance
• ·Other directly attributable lifecycle costs

AI ROI measures the return on the overall AI investment, rather than only its recurring operating expense. It answers: 'What economic return did the enterprise generate relative to the total investment required to create and operate the capability?'

AI Compute Intensity

AI Compute Intensity = Total AI compute consumed by the workflow ÷ Successful outcomes produced

The workload consumes an average of 14.1 measured GPU-seconds to produce one successful outcome.

AI Compute Intensity measures the computational resources required to produce one useful outcome.

It allows engineering teams to compare models, agent architectures, routing strategies, and inference environments using a common workload efficiency measure.

The metric should capture the relevant compute consumed across the execution path, including model inference and other material AI compute required to complete the workflow.

Raw GPU time should be used primarily for comparisons on equivalent or normalized infrastructure. GPU-seconds across different accelerator generations or architectures are not inherently equivalent because throughput, memory, power efficiency, and model performance can differ materially.

For cross-infrastructure economic comparisons, use Cost per Successful Outcome as the more portable business measure.

Calculate:

Model inference + embeddings + retrieval + storage + networking + observability + supporting platform costs

The external API deployment carries $5,200 in recurring monthly operating cost with no infrastructure commitment.

Best suited to workloads where elasticity, rapid model access, and low infrastructure commitment carry significant value.

GPU Rental Cost

GPU Rental Cost = Number of GPUs × Provisioned Hours per GPU × Rate per GPU Hour

The organization incurs $8,000 of raw GPU rental expense for the provisioned capacity before adding storage, networking, orchestration, observability, and other supporting infrastructure costs.

Then add:

Storage + networking + orchestration + observability + engineering operations + idle capacity

The raw GPU rental represents only two thirds of the true infrastructure cost once supporting services and idle capacity are included.

Utilization becomes a central economic variable.

Effective Productive GPU Hour Cost — Rented

Effective Productive GPU Hour Cost = Total GPU Infrastructure Cost ÷ Productive GPU Hours

After utilization and supporting infrastructure are considered, each productive GPU hour effectively costs $18.46.

Productive GPU Hours represent GPU time actually utilized serving the intended workload. Idle provisioned capacity remains part of infrastructure cost but does not enter the productive-hour denominator.

This metric should be evaluated alongside AI Compute Intensity from Section 02 and AI Cost per Successful Outcome from Section 01.

Together, the framework creates the economic chain: Infrastructure Cost → Effective Cost of Productive Compute → Compute Required per Successful Outcome → Cost per Successful Outcome → Economic Value and ROI.

Base Reserved Compute Commitment

Base Reserved Compute Commitment = Number of Committed GPUs × Committed Hours per GPU × Contract Rate per GPU Hour

The enterprise has committed $876,000 annually for GPU capacity before supporting infrastructure and operating costs.

Full reserved infrastructure cost should also include relevant storage, networking, orchestration, software, operations, and other contractual charges. Unused committed capacity remains an economic cost and creates utilization exposure.

• ·Unused capacity
• ·Demand variability
• ·Minimum commitments
• ·Scaling constraints
• ·Contractual flexibility
• ·Migration costs

Reserved capacity can improve unit economics while increasing commitment exposure.

Effective Productive GPU Hour Cost — Owned Fleet

Effective Productive GPU Hour Cost = Total Lifecycle Cost of GPU Fleet ÷ Total Productive GPU Hours over Economic Life

Total Lifecycle Cost includes, where applicable: hardware acquisition + financing or cost of capital + power + cooling + networking + storage + facilities allocation + platform software + operations + maintenance + expected failures − residual value.

After acquisition, operations, utilization, financing, and residual value are incorporated, the owned fleet costs $15.00 for each productive GPU hour delivered.

This allows a more disciplined comparison against rented or reserved infrastructure for a comparable workload.

Hardware ownership becomes attractive when utilization, workload duration, and infrastructure efficiency support the capital commitment.

Break Even Utilization

Break Even Utilization = Annual Fixed Cost of Owned or Committed Infrastructure ÷ (Annual On Demand Cost at 100% Utilization − Annual Variable Cost of Owned or Committed Infrastructure at 100% Utilization)

Under these assumptions, the owned or committed infrastructure reaches economic break even against on demand capacity at approximately 50% utilization.

This is a simplified break even model. A full investment decision should also incorporate financing, contract structure, migration costs, capacity constraints, technology refresh risk, and other material lifecycle considerations.

Incremental NPV of Migration

Incremental NPV of Migration = Present Value of Future Costs if Staying − Present Value of Future Costs if Migrating + Present Value of Incremental Economic Benefits from Migration

The migrating scenario should incorporate migration engineering cost, remaining contractual obligations, stranded asset cost, residual value recovered, new infrastructure cost, and the operating cost of the replacement environment.

Migration creates $3.5M of incremental economic value before applying the enterprise's required investment hurdle.

A positive Incremental NPV indicates that migration creates economic value relative to staying. The final investment decision should also satisfy the enterprise's required return or investment hurdle.

The Stay and Migrate scenarios should be structured so that costs, residual values, stranded assets, and migration costs are counted once and are not double counted.