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Cloud & AI Cost
Optimization

Spending on cloud and AI is rising faster than most organizations can measure it. The companies that win are the ones that treat cost management as a strategic discipline, not a finance problem.

Overview

You Can't Optimize What You Can't See

Cloud and AI costs share the same structural problem: they accumulate fast, they're easy to misattribute, and by the time they appear on a finance report, the spending decisions that caused them are months old. Most organizations don't have a cost problem. They have a visibility problem.

I help companies identify where costs are actually coming from, build the accountability structures to manage them in real time, and establish the optimization disciplines that prevent waste from compounding as workloads and AI features scale. This is not a tool implementation engagement. It is a strategic and operational practice built around how your business actually makes spending decisions.

The goal is not to spend less. It is to spend accurately, with full visibility into what each dollar of cloud and AI infrastructure is actually producing for the business.

The Problem

Where Cloud and AI Costs Accumulate Without Accountability

Invisible AI Inference Costs

Token consumption at production scale is non-linear and rarely modeled before deployment. A feature that looks affordable in a pilot can become a significant cost driver once it runs against real user volume, with no mechanism to attribute the spend back to a specific product decision.

Unattributed Cloud Spend

Compute, storage, and data transfer costs pool into shared infrastructure accounts where no product team owns a specific line item. Engineering makes architectural decisions with real cost implications that finance cannot see until the invoice arrives.

Consumption Model Blindspots

The shift from per-seat licensing to consumption-based pricing has made AI and cloud cost forecasting genuinely difficult. Budgets built on old assumptions applied to new pricing models are budgets built on sand.

Scaling Without Cost Architecture

Growth-stage and PE-backed companies scale workloads and add AI features faster than they build the cost attribution infrastructure to understand what they're spending. The discipline gap compounds: every quarter without attribution is a quarter of decisions made without economic signal.

Reserved Capacity Misalignment

Reserved instance and committed use discount strategies require accurate workload forecasting. Organizations that over-reserve waste capital. Those that under-reserve pay on-demand rates for predictable workloads they could have discounted significantly.

Third Party and SaaS Sprawl

Cloud vendor relationships are one layer of a much larger cost picture. Data platforms, observability tools, security vendors, and AI APIs each add consumption-based costs that aggregate into significant spend with no consolidated view of total technology cost of ownership.

How I Help

Building a Cost Management Practice, Not a One-Time Audit

Cost Visibility Assessment

A structured review of your current cloud and AI spend across all providers and workloads. I identify where costs are attributed, where they are pooled, where visibility is missing, and which spending patterns represent the highest optimization opportunity.

Attribution Architecture

Designing and implementing the tagging strategy, account structure, and tooling that makes every dollar of cloud and AI spend traceable to a product, team, or feature. This is the foundation that makes all downstream optimization work possible.

AI Feature Cost Modeling

Applying the Feature Economics Model to your AI portfolio — projecting production-scale inference costs before deployment, building per-feature P&Ls, and establishing the review cadence that keeps leadership informed of unit economics as workloads evolve.

Optimization Roadmap

A prioritized plan for reducing waste across compute, storage, data transfer, and AI workloads. Includes reserved capacity analysis, rightsizing recommendations, architectural changes with quantified impact, and vendor renegotiation guidance.

FinOps Practice Build

Standing up the organizational discipline to sustain cost management over time — defining ownership, establishing review cadences, creating the reporting that connects engineering decisions to financial outcomes, and building the culture of cost accountability.

Board and Investor Reporting

Translating cloud and AI spend into the language boards and PE sponsors need — unit economics, cost per customer, infrastructure margin, and the relationship between technology investment and business outcome. Making the numbers defensible and the trends actionable.

What You Get

Areas of Focus

Let's Talk

Do You Know What Your Cloud and AI Is Actually Costing You?

Most organizations don't — and the gap between what they think they're spending and what they're actually spending gets more expensive every quarter. Let's start with a conversation.

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