Home / Decisions / AI Infrastructure Audit

Decisions · diagnosis first

What you actually have — and what's wrong with it

An honest snapshot of your current infrastructure: what's deployed, how it's loaded, what it costs today, where it idles, where it bottlenecks, and where the hidden risk is. This is a diagnosis, not a prescription. We won't tell you to buy — we'll tell you what you actually have.

Request an audit See a sample profile →

On the tableA profile across six risk axes, a decomposed workload, bottlenecks and risk points, readiness facts, and an honest data boundary.

Who it's forInfrastructure VP / CIO / CTO. Often initiated by FinOps.

EngagementTimeline [timeline] · Price [price]

Why this comes first. Any major decision — exit, buy, hold — rests on one base: what you have and what state it's in. Right now that base doesn't exist, and decisions get made blind. The Audit is the price of entry to a meaningful choice, not insurance against a mistake.

The moment you arrive here

You inherited an infrastructure and you're not entirely sure what's inside it. Ten years of cloud migrations washed out the people who could honestly say what your stack really costs and how it's loaded. The CFO owns the capital, the CTO owns the technology, Legal owns compliance — and nobody sees the whole picture.

FinOps answers "what does this cost right now," but not "what state is it in and where's the risk." And ahead of you is a budget cycle, a new mandate, or a board question — "why is this so much." Before you decide anything large, you need one base you can trust. Right now there isn't one.

What you get

A snapshot of the present across your whole estate — observed state, not a forecast and not a decision. In your hands:

One honest base you can trustWhat's deployed, how it's loaded, what it costs today by actual invoices — not by vendor list price.

Where it idles and where it bottlenecksReal bottlenecks — and "value per dollar" next to "cost per hour of hardware"; we show the gap between them as an inefficiency signal.Compute Yield

A workload profile — decomposed, not averagedFloor, peaks and one-off spikes shown separately. The average hides exactly what half of any decision rests on.Workload Characterization

Risk points nobody flaggedHardware near end-of-support, single points of failure, hidden dependencies, single-supplier exposure.

A profile across six risk axesState on each: real cost, how stuck you are, what demand you're actually betting on by utilization, which hardware you sit in, where you stand legally and physically, how fresh the fleet is.CCS — True Cost · Anchor · The Bet · Supply · Ground · Shelf Life

Whether the org can change — by factIaC and CI/CD, on-call rotations, when recovery was last tested, how many engineers with verified GPU / Kubernetes experience. As a list of facts and gaps.Execution Readiness — facts only

An honest data boundaryWhat we rejected at intake and why — you see what didn't make it into the picture.

What the Audit does not do — and why that's the point

It doesn't rule "buy / exit / stay." This is a diagnosis, not a decision. The decision is the next, separate step.

It doesn't issue a score or verdict. A profile by factor is about the present; the number and verdict are assembled higher up, in the decision products.

It doesn't pull you toward a purchase. Even when the diagnosis obviously hints "buy more," we show the gap — we don't write the order.

That's the value: a hardware supplier that, during an audit, doesn't turn diagnosis into an invoice for servers. The truth about what you have first — decisions later, and separately.

What you'll actually receive

A one-page state snapshot. Sample — form, not real data.

Sample · illustrative form

Infrastructure profile

Workload ‹name› · period ‹window› · observed state, no score

True Cost‹$/mo actual›

Compute Yield‹$/1M tokens› vs ‹$/GPU-hour›, gap ‹…›

AnchorLock-in: egress, managed services, data ‹…›

The BetUtilization: floor ‹N%›, peaks ‹M%›

SupplyFleet ‹mix, vendors›

GroundJurisdictions & power ‹…›

Shelf LifeGeneration ‹…›, EOS in ‹…›

Workload: baseline ‹…› · peak ‹…› · anomaly ‹…› · excluded ‹… and why›

Bottlenecks: ‹…› · Risk points: ‹EOL, SPOF, dependencies›

Readiness (facts): IaC ‹yes/no› · last recovery test ‹date› · engineers with GPU experience ‹N›

Data boundary: rejected ‹what› — because ‹reason›

There is, and will be, no Keel Score, Go/No-Go verdict, or recommendation to buy here.

Why this snapshot can be trusted

The Audit is the first point of contact with your raw data, so the filter sits here, earliest of all. Only real invoices and production telemetry enter the diagnosis. Vendor prices and decks, lab benchmarks and best-case slices are rejected at intake — even if they're put on the table as "this is what it costs." What's rejected is visible in the report. (This is the Data Admissibility rule of the open Compute Capital Standard, CCS.)

A real example of an honest diagnosis:
[[FILL: one anonymous but real example where the Audit showed state and stopped — e.g., found the problem wasn't where the client thought, or that the bottleneck is solvable without a purchase. No verdict, diagnosis only. This is the Audit's honesty signal.]]

Decisions, site-level — standard & team:
See the shared standard-proof and "who's behind this" blocks (same as on the Cloud Exit page). [[FILL: link to CCS Public Reference + version; team / track record.]]

The diagnosis comes before the decision — and stays separate from it.

Audit feeds the choice that follows; it never makes it for you.

This is for you if

The Audit is an entrance, not a filter. It turns almost no one away; the only question is whether now is your moment. It's for you if:

a new technical leader has arrived and is sorting through what they inherited;

you're heading into a budget cycle and need honest numbers;

the board is asking "why is this so much," and there's no single answer;

accountability is spread thin, and nobody sees the whole picture;

a "build or exit" decision is ahead, and you need a base to build it on.

How the work runs

STEP 1

We take inAccess to real invoices for a representative period (not decks), production telemetry, an inventory of the current fleet, and a dependency map.

STEP 2

We captureBy the CCS standard: decompose the workload, profile the six axes, gather readiness facts, fix the data boundary.

STEP 3

We hand overThe profile, bottlenecks, risk points and an honest data boundary.

STEP 4

Timeline[timeline]

What's next

From this base, the next question grows naturally — exit or build. The paths from here:

Audit feeds these decisions — its output is the input for them.

Start with the diagnosis

Tell us a little about your environment — we come back with a profile, bottlenecks, risk points and an honest data boundary.