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AI Insights

Measure AI developer productivity — the real Claude Code ROI

Productivity multipliers you can take to a CFO. 1.8× team average, 14.3× peak, 0.8× on legacy code — numbers you can only get by measuring.

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The problem

The measurement crisis

  • Google DORA 2024: PR review up 441%, bugs per dev up 54%, incidents per PR up 242% — in AI-heavy orgs.
  • Vendor dashboards report tab-completion counts — vanity metrics no CFO can defend.
  • Without per-project attribution, the AI ROI question is unanswerable — the result depends entirely on which team and which codebase.
How CloudByte answers it

Real multipliers, per developer, per project, per model

Session data + git history + token spend — attributed per developer, project, and model. DORA-aligned.

Token spend — last 30 days
Grouped by developer · USD
Opus Sonnet Haiku
RM
Raj Mathur
$204.18
PS
Priya Sharma
$172.36
AC
Alex Chen
$108.42
MP
Maya Patel
$82.60
ST
Sam Taylor
$44.12
Opus $15/$75Sonnet 4.6 $3/$15Haiku $0.80/$4in/out per 1M tokens

Productivity multiplier

Measured, not claimed. Pre-AI commit cadence is the baseline; we report the true multiplier per developer and project — separating backend (high) from infrastructure (flat) from legacy refactor (often negative).

DORA overlay

All four DORA metrics with AI-session volume as a second axis. See whether AI lifted deployment frequency, flattened change-failure rate, or made things worse.

Cost per commit

Token spend on Opus ($15/$75 per million), Sonnet ($3/$15), and Haiku ($0.80/$4) divided by commits produced — per developer, project, and week.

Our 30-day pilot

What AI insights revealed on our own team

The range is the insight.

0.0×
Team-wide avg
Measured, not claimed
0.0×
Peak project
Best-performing codebase
0.0×
Legacy code
AI slowed us down here
$0
Shelfware recovered
Per month
For CTOs / VP Eng

Numbers the CFO accepts

  • Productivity multipliers with confidence intervals — not marketing slogans.
  • DORA + SPACE overlays fit your existing engineering KPI deck.
  • Cost-per-commit attribution for quarterly budget reviews.
For Security / Compliance

Spend trails are audit trails

  • Every token tied to a user, project, and prompt. Full audit path.
  • Anomaly detection on spend spikes — catch runaway scripts before the invoice.
  • Per-seat hard caps via API — no single developer burns the month's budget.

Works with your existing engineering KPI stack

Claude Code
GitHub
Linear
Jira
PagerDuty
Grafana

Related features

Activity Tracking →

The capture layer that feeds the productivity multipliers.

Git Tracking →

AI-summarized commits that make review faster without sacrificing oversight.

FAQ

Questions engineering leaders ask about AI ROI

Turn AI spend into defensible numbers

A 15-minute demo with a founder. See real multipliers on a real dashboard.

Book a demoRead the pilot data