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What is AI Infrastructure Debt?

AI Infrastructure Debt (AID) captures the long-term cost created when organisations make short-term AI infrastructure decisions without accounting for scale, portability, compliance and future workload requirements. It functions similarly to technical debt — invisible in the near term but compounding over time.

What is the Inference Waste Ratio?

The Inference Waste Ratio (IWR) measures the proportion of AI inference spend attributable to inefficient model routing, poor prompt engineering, absence of caching or over-specified hardware. Organisations without optimisation typically have IWR scores indicating 25–45% of inference spend is avoidable.

Proprietary Frameworks

RaisePath Intelligence Frameworks

RaisePath has developed four proprietary frameworks for understanding, measuring and communicating AI infrastructure economics. Each provides a structured lens through which AI compute cost, risk and waste can be assessed and reported at board level.

GLI · GPU LEAKAGE INDEX

Measuring the AI compute budget that never reaches its intended workload.

The GPU Leakage Index (GLI) is a RaisePath framework for quantifying the proportion of AI compute budget that is lost through underutilisation, misallocation, or poor procurement decisions — before it generates any productive output.

Framework components

Utilisation Leakage

GPU capacity provisioned but not used. Common in organisations that provision for peak demand and run at 20–40% average utilisation.

Allocation Leakage

GPU compute assigned to the wrong workload type — for example, using H100s for workloads that a lower-cost GPU would handle equivalently.

Procurement Leakage

Cost premium paid over the market rate for equivalent capacity. Occurs when organisations default to hyperscale on-demand pricing without exploring reserved or dedicated alternatives.

Routing Leakage

Inference workloads sent to GPU infrastructure when cheaper CPU-based or serverless routes would provide equivalent latency and quality.

Why it matters

The GPU Leakage Index provides a single quantified view of GPU budget efficiency. An organisation with a high GLI score is losing a meaningful proportion of its compute budget before any productive AI work is done. It is designed to create a board-level metric that reflects infrastructure management quality, not just spend.

Industry Benchmark

Organisations with no structured compute review typically show GLI scores indicating 30–50% leakage. After a RaisePath compute audit and optimisation programme, GLI scores typically fall to 10–20%.

Apply This Framework

A RaisePath AI Compute Audit applies all four frameworks to your organisation's infrastructure to produce a quantified view of waste, risk and optimisation opportunity.

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All four frameworks at a glance

Apply the RaisePath frameworks to your organisation

An AI Compute Audit uses all four frameworks to produce a quantified, board-ready view of your infrastructure cost position, waste profile and optimisation roadmap.

Book an AI Compute Audit Read AI Compute Insights →