The leadership blind spot

Four questions. Four tools. No single answer.

Your AI-era engineering org is generating cost and output faster than any one dashboard can explain. The answers live in separate tools that never talk to each other.

Are we shipping faster, and is it stable?

DORA lives in one delivery tool your CFO never opens.

Delivery analytics

Is the AI we bought paying off?

Copilot, Cursor and Claude each have their own dashboard.

Per-tool AI dashboards

Where is the cloud spend going?

AWS, Azure and GCP bills sit in three separate consoles.

Cloud consoles

What is the actual ROI?

Nobody can tie spend to output in a number you can defend.

A spreadsheet, quarterly

One board, four worlds

Everything an engineering leader measures, on one board.

G360 connects your whole environment and grounds it on GKS, so delivery, AI spend, cloud cost, and unit economics finally sit in one place.

Engineering delivery

Is delivery fast, stable, healthy?

DORA, CI/CD, pull-request and commit metrics, graded and trended, so you see the engineering heartbeat at a glance.

DORACI/CDPR & commitFlaky tests

AI-coding adoption & ROI

Is the AI paying off?

Adoption, token spend, acceptance rate and a defensible ROI across Copilot, Cursor, Claude Code, and every LLM key.

CopilotCursorClaude CodeLLM spend

Cloud FinOps

Where is the spend going?

AWS, Azure and GCP normalized into one view: spend, anomalies, budgets, rightsizing, and savings you can act on.

AWSAzureGCPAnomalies

Unit economics

Does it pencil out?

Cost per customer, cost per feature, gross margin, ROI and payback, tying every dollar of spend back to the business.

Cost / customerGross marginPaybackChargeback

The number leaders can defend

Every $1 of AI spend, traced to a return.

G360 turns tool invoices and time saved into an ROI multiplier, a break-even date, and a monthly savings figure, computed from your own usage, not a vendor slide.

Investment in, savings out, on one screen.

Per-seat cost, active users, developer hourly cost and hours saved become a live ROI model. It answers the question every AI budget eventually faces: was it worth it?

ROI multiplier and break-even

"Every $1 invested returns $3.20" and "break-even in 41 days", updated daily.

Monthly and annual savings

Time saved converted to money at your real developer cost, not a blended guess.

Investment vs. savings, trended

Net gain plotted over time so the payoff is obvious to a non-technical buyer.

Claude Code ROI Dashboard

Investment, time saved, cost saved, and return

Monthly

ROI Multiplier

3.2×

$1 → $3.20

ROI Percentage

220%

return on invest

Break Even

41d

to recover

Active Users

45

of 80 licenses

Investment vs Savings Comparison

Savings Investment Net gain +$18.9K / mo

The spend nobody is watching

You are paying for seats nobody is using.

G360 reads licensed vs. active seats across every tool and surfaces the waste. It is usually the fastest money a leader can reclaim.

Tool Cost Settings

Licensed vs. active seats · seat-level waste

Live

Tool	Licensed	Active	Util.	Monthly	Wasted
Claude Code	60	51	85%	$1,800	$270
OpenAI Codex	45	34	76%	$1,125	$275
Cursor	40	28	70%	$800	$240
GitHub Copilot	80	45	56%	$1,520	$665
GitHub	120	87	73%	$2,520	$693

            Reclaimable across all tools$2,143 / mo
          

Seat waste, surfaced automatically.

Every connected tool reports licensed seats against contributors active in the trailing window. G360 does the math: utilization, monthly spend, and the exact dollars leaking to dormant seats.

Utilization at a glance

Green above 80%, amber above 60%, red below, across every tool you pay for.

Wasted spend, quantified

Inactive seats × cost per seat, ready to export and take to a renewal.

What is your team leaving on the table?

Your AI-coding tools

Licensed seats80

Actively using them56%

Cost per seat / month$19

Hours saved / active dev / day1.0

Developer cost / hour$75

What G360 would surface

$832K

net annual value from your AI-coding tools

Value of time saved$851K / yr

Wasted on dormant seats$7,980 / yr

Total tool cost$18,240 / yr

Return on tool spend47× / $1

Illustrative model using the app's own ROI method: time saved × active devs × working days × developer cost, less total license spend. Waste = inactive seats × cost per seat. Your real numbers come from connected usage.

For the CTO

Is AI actually making delivery faster and safer?

DORA, CI/CD and pull-request health in one view, graded against industry benchmarks, so speed never comes at the cost of stability.

DORA Metrics

Software delivery performance

Last 30 days

Deployment Frequency

4.2 / day

Elite

Lead Time for Changes

18 hrs

High

Change Failure Rate

22%

High

Mean Time to Restore

52 min

Elite

The engineering heartbeat, graded.

Every DORA metric is banded Elite, High, Medium or Low against the research benchmarks, and CI/CD tells you why. When change-failure creeps up, the flaky-test count usually explains it, on the same screen.

Pipeline success

94%

Target 95%

Avg build time

6m 40s

▼ 12%

Flaky tests

17

▲ 4

PR adoption & leaderboards

See who is shipping, review load, and where the review bottleneck sits.

For the CFO

AWS, Azure and GCP, normalized into one number.

Multi-cloud spend, anomalies, budgets, rightsizing and unit economics on one board, tied to customers, features and margin.

Multi-Cloud FinOps

AWS · Azure · GCP

MTD

Spend MTD

$487K

▲ 8% MoM

Forecast EoM

$612K

vs $600K

Open anomalies

37

$312K impact

$435KTotal

Virtual Machines$164K

Databases$121K

Storage$88K

Networking$62K

FinOps that ties cost to the business.

Beyond spend and anomalies, G360 carries the unit economics a finance team actually asks for: what a customer costs to serve, and what margin is left.

Cost / customer

$4.12

▼ 7% QoQ

Gross margin

71%

▲ 1.2 pts

Savings pipeline

$12.5M

▲ 18%

Payback

2.4 mo

▼ 0.6 mo

Adoption across every tool

Who is actually using the AI you bought?

Adoption, acceptance and cost per developer across Copilot, Cursor, Claude Code, OpenAI and Anthropic, in one comparable view.

AI Coding & LLM Adoption

Active users, acceptance, and cost per developer

90 days

Active developers

36

▲ 8

Tool acceptance

61%

▲ 4 pts

Cost / user

$16.6

per month

Completions / $1

3.1

above target

Claude Code

51 / 60 active

OpenAI Codex

34 / 45 active

Cursor

28 / 40 active

GitHub Copilot

45 / 80 active

The differentiator

The executive summary writes itself.

G360 reads every dashboard, writes the board-ready summary, and delivers it on a schedule. Most leaders never log in. The insight comes to them.

G

Executive Summary

Auto-generated · week of this Monday

AI-generated

ROI & Impact

AI-coding tools returned 312% this quarter with a 2.4-month payback. Reclaiming $2,197/mo of dormant seats would lift net savings a further 9% with no impact on output.

Operational Risks · 2 identified

HIGHChange failure rate trending toward Medium

CFR rose from 16% to 22% over three weeks. Mitigation: 17 flaky tests concentrated in the checkout pipeline are the leading cause; quarantine and stabilize.

MEDIUMCloud forecast will breach budget

EoM forecast $612K vs $600K budget. Mitigation: 37 open anomalies carry $312K of impact; the top VM anomaly alone is +152%.

Recommended Actions

P1

Reclaim 35 dormant Copilot seats before the annual renewal ($665/mo).

P2

Stabilize the checkout test suite to pull change-failure back to Elite.

P3

Rightsize the flagged VM scale set for an estimated $910/mo saving.

Delivered on your schedule to Email Slack Teams as a PDF or PNG.

Connects to everything

20+ sources, one board.

G360 reads from the tools you already run, through GKS, so the whole picture assembles itself.

Plus webhooks, and more connectors landing continuously.

Governed by GKS

One brain. Your data stays yours.

G360 reads through GKS, the governed boundary you control, and runs on the same shared brain as the rest of the Garth suite.

Reads through the boundary

Each connector streams only what the dashboard needs, filtered at intake. Nothing you have not connected ever enters.

Runs where you run

Garth Cloud, in your VPC, or fully on-prem. Your systems stream in over a governed, encrypted channel.

One shared brain

The same GKS graph that grounds GReview, GScan and GRelease. Connect once, and the whole suite gets smarter.

Where G360 stands apart

They each own a slice. G360 owns the board.

Delivery tools show delivery. FinOps tools show cloud. Per-tool dashboards show one vendor. G360 grounds all of it on one brain.

Capability	G360	Delivery tools	FinOps tools	Per-tool AI dashboards
Engineering delivery (DORA, CI/CD, PR)	✓	✓	–	–
AI-coding tool adoption & ROI	✓	~	–	~
Seat-level license waste	✓	–	–	–
Multi-cloud FinOps & unit economics	✓	–	✓	–
AI executive summary, auto-delivered	✓	–	–	–
One shared brain across the suite (GKS)	✓	–	–	–

✓ Full~ Partial– None

Point tools measure a slice. G360 measures the whole org.

Pricing

Priced for the platform, not per login.

G360's value scales with your whole org, not seat count, so it is priced on a flat platform fee in coarse population bands. Growth inside a band is free.

Starter

$1,500/ mo

Up to 50 developers

3 connectors, 3 boards
Basic AI Impact & ROI
DORA & delivery health

Book a Demo

Most teams start here

Growth

$3,000/ mo

Up to 150 developers

5 connectors, 10 boards
Full AI Impact & ROI
Cloud FinOps & seat waste
Scheduled executive summaries

Book a Demo

Scale

$6,000/ mo

Up to 400 developers

8 connectors, 25 boards
Forecasting & unit economics
+$2,000 per 200 devs to 1,000

Book a Demo

Enterprise

$10K+/ mo

Custom scale

SSO, audit, BYOK
Private cloud & on-prem
Custom connectors & SLA

Contact sales

Included in the Garth Suite at a bundle price below buying each product apart. BYOK drops the AI portion of the bill about a third.

Prove the ROI
your AI promised.

Four questions. Four tools. No single answer.

Everything an engineering leader measures, on one board.

Engineering delivery

AI-coding adoption & ROI

Cloud FinOps

Unit economics

Every $1 of AI spend, traced to a return.

Investment in, savings out, on one screen.

You are paying for seats nobody is using.

Seat waste, surfaced automatically.

What is your team leaving on the table?

Your AI-coding tools

What G360 would surface

Is AI actually making delivery faster and safer?

The engineering heartbeat, graded.

AWS, Azure and GCP, normalized into one number.

FinOps that ties cost to the business.

Who is actually using the AI you bought?

The executive summary writes itself.

20+ sources, one board.

One brain. Your data stays yours.

Reads through the boundary

Runs where you run

One shared brain

They each own a slice. G360 owns the board.

Priced for the platform, not per login.

Stop guessing what your AI-era org costs and returns.