AI Code Cost Tracker Dashboard

3.85

Derivation Chain

Step 1 AI coding tool proliferation (Writing code is cheap now)

→

Step 2 AI code generation cost management SaaS

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Step 3 Per-transaction AI code generation cost & quality tracking dashboard

Problem

Development teams of 10-50 using AI coding tools (Cursor, Copilot, Claude Code, etc.) incur ~$75-$225/mo per developer in AI API call costs, but have zero visibility into which projects, developers, or tasks are driving the spend. Without cost optimization, tens of thousands of dollars are wasted annually, and CTOs have no data to report AI tool ROI to executives.

Solution

Integrate GitHub/GitLab commit logs with AI tool API usage logs to auto-aggregate AI code generation costs by project, developer, and task. Visualize adoption rate (percentage of generated code actually committed) and cost-vs-productivity metrics on a dashboard. Auto-generate monthly AI ROI Reports for executive reporting.

Target: CTOs and VPs of Engineering at 10-50 person IT Startups/agencies, ages 30-40

Revenue Model: SaaS monthly flat rate ~$11/mo per developer seat, 20% volume discount for 10+ seats, free tier for 5 seats

Ecosystem Role: Infrastructure

MVP Estimate: 2_weeks

NUMR-V Scores

N Novelty

3.0/5

U Urgency

4.0/5

M Market

4.0/5

R Realizability

4.0/5

V Validation

4.0/5

NUMR-V Scoring System

N Novelty	1-5	How uncommon the service is in market context.
U Urgency	1-5	How urgently users need this problem solved now.
M Market	1-5	Market size and growth potential from proxy indicators.
R Realizability	1-5	Buildability for a small team with realistic constraints.
V Validation	1-5	Validation signal quality from competition and demand data.

SaaS N=.15 U=.20 M=.15 R=.30 V=.20 Senior N=.25 U=.25 M=.05 R=.30 V=.15

Feasibility (69%)

Tech Complexity

29.3/40

Data Availability

19.4/25

MVP Timeline

20.0/20

API Bonus

0.0/15

Feasibility Breakdown

Tech Complexity	/ 40	Difficulty of core implementation stack.
Data Availability	/ 25	Practical availability and cost of required data.
MVP Timeline	/ 20	Expected time to ship a usable MVP.
API Bonus	/ 15	Bonus for viable public API leverage.

Market Validation (62/100)

Competition

8.0/20

Market Demand

6.2/20

Timing

17.0/20

Revenue Signals

10.5/15

Pick-Axe Fit

12.8/15

Solo Buildability

7.0/10

Validation Breakdown

Competition	/ 20	Signal quality from competitor landscape.
Market Demand	/ 20	Demand proxies from search and mention patterns.
Timing	/ 20	Fit with current shifts in tech, behavior, and regulation.
Revenue Signals	/ 15	Reference evidence for monetization viability.
Pick-Axe Fit	/ 15	How well the concept serves participants in a trend.
Solo Buildability	/ 10	Practicality for lean-team implementation.

Technical Requirements

Backend [medium] Frontend [medium] Data Pipeline [low]

Dashboard