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LLM ROI

LLM Infrastructure ROI

Know what local models save you.

Tracks the cost and time savings AlloyGRC AI features produce against traditional manual effort, updated in real time, and compares on premises GPU spend with cloud GPU instances and per token commercial model pricing, both projected from actual token consumption.

Sovereign AI, priced

Running models inside your boundary is a security decision first. This dashboard makes it a budget decision too. Every projection updates in real time and can be broken down by app, system, user or model, over a window you set yourself.

The same number security bought you, now the number finance wants to see.

How it works

How a token turns into a dollar figure

The stages that turn raw model usage into the cost and ROI numbers on the dashboard.

STAGE 01

Usage collection

Token consumption is collected as AI features are used, tagged by model, application, system and user.

Usage log by model, app, system and user

STAGE 02

Cost projection

Usage is priced two ways at once: against on premises GPU cost, including an amortized hardware cost, and against the commercial per token pricing of each model.

Projected cloud GPU and API cost, single GPU and multi GPU

STAGE 03

ROI calculation

Projected cost is weighed against traditional manual effort to produce a cost savings figure and a percentage ROI, refreshed in real time as usage changes.

Cost savings figure and ROI percentage

Pipeline detail pending confirmation from the team that owns the cost model.

At a glance

KPI cards for the numbers leadership asks for

$4,200

Projected cloud compute cost, single GPU

$15,600

Projected cloud compute cost, multi GPU

$2,100

Amortized on premises hardware cost

$1,827

Savings due to use of On-Premise GPU

87%

ROI on Local verses Commercial Cloud Compute

$1,509

Savings due to use of On-Premise LLMs

Illustrative dashboard. Card types are real, the values are invented.

Every way that matters

One dashboard, broken down four ways

Both cost projections, cloud GPU and per token API, are cut by the same four dimensions, over whatever window you set.

Cut by

  • By app

    Which application drove the usage and the cost behind it

  • By system

    Which system ran the workload, for tracing cost back to infrastructure

  • By user

    Who generated the usage, down to the individual

  • By model

    Token usage broken out model by model, not blended into one total

30 day cloud compute utilization trend

Daily cloud GPU utilization over the trailing 30 days, or whatever window is set.

Per app compute usage

Compute usage compared across applications.

Per model projected cost

Projected cost ranked model by model.

Illustrative dashboard. Chart types are real, the data shown is not.

What you can do

  • Pull a savings figure for a leadership update

  • Compare on premises GPU cost against cloud GPU instances and per token pricing

  • Filter any breakdown or chart to a custom date range

Seen in the app

The real thing, on synthetic data

The LLM ROI dashboard: six KPI cards for cloud GPU cost, enterprise cloud GPU cost, on premises cost, net savings and two ROI percentages, a bar chart of cloud cost equivalent per app, and a line chart of the daily cost trend over the last thirty days.
The dashboard on a test instance: six KPI cards, cloud cost equivalent per app, and the daily cost trend over thirty days. Captured from the real product; the workload behind the figures is a development instance, so the numbers are illustrative rather than a customer's.

AI Innovation

Also in this area

Plain English in, real answers out, on your hardware

How it all fits together

LLM Infrastructure ROI has more to show than fits on this page yet. A full walkthrough with visuals is being written. Ask for a demo to see it running today.

Put a number on sovereign AI.

Ask for a demo and see the comparison for an environment like yours.

Request a Demo The features the savings come from