Copilot Billing Forecast

A client-side toolbox for analyzing and forecasting GitHub Copilot AI usage and spend. Upload a GitHub usage report CSV and explore visualizations, forecasts, and per-user and per-cost-center insights - all entirely in your browser.

🔒 Your data never leaves your browser. All CSV parsing, analysis, and forecasting happen client-side - there is no server-side processing, upload, or persistence beyond the current browser session. See CONTRIBUTING.md for the full privacy constraint and development guidelines, and docs/analytics.md for what anonymous analytics are recorded.

⚠️ This is not an official GitHub product. It is an unofficial tool to help customers analyze and forecast their GitHub Copilot AI usage and spend. Figures are estimates - always refer to your GitHub billing statements as the source of truth.

Overview

GitHub Copilot bills AI usage in AI Credits, and it can be hard to tell where those credits are going or whether you're on track to stay within your entitlement. Copilot Billing Forecast turns a GitHub usage report into clear, interactive insights: it projects where your spend is heading, highlights your heaviest users, models, and cost centers, and surfaces unusual spikes - so you can spot overages before they happen and plan budgets with confidence.

Everything runs in your browser. You upload a usage report once and switch between the tools below without your data ever leaving your device.

This tool is intended for GitHub Copilot Business and Enterprise plans, which provide the organization and enterprise usage reports it relies on. Individual Copilot plans (Free/Pro/Pro+) don't produce these reports and aren't supported.

Tools

• Usage Forecast - Forecast AI Credit consumption and track it against your entitlement, with a confidence band, trend indicator, projected exhaustion date, and any overage in USD.
• Team Insights - Per-user metrics, models, and budget forecasts, including a spend distribution and per-user month-end projections against an optional budget.
• Model Breakdown - AI Credit usage and trends broken down by model.
• Spike Detection - Flag days with anomalous usage above the trend.
• Cost Center Rollup - Roll up AI Credit usage and budgets by cost center.

How the forecast range is calculated

The Usage Forecast fits an ordinary least squares linear regression to your daily AI Credit usage and projects that trend forward over the horizon. The shaded band around the projection is an approximate 95% prediction interval, calculated as follows:

• The fitted line gives the central forecast value for each day.
• The band half-width at a given day is t · SE · √(1 + 1/n + (x − x̄)² / Sₓₓ), where SE is the residual standard error of the fit, n is the number of days used, and the square-root term is the standard prediction-interval leverage factor (the band widens the further you project from the observed data).
• t is an approximate 95% critical value that widens for small samples (≈4.3 for ≤2 days down to 1.96 for >30 days) to account for added uncertainty.
• Both bounds are floored at zero since daily usage can't be negative, and the cumulative projected total is reported as a range (projectedLower to projectedUpper) by summing each day's bounds.

A couple of adjustments keep the run rate realistic:

• The most recent day is excluded from the regression when at least three earlier days remain, because the report is typically pulled mid-day and its partial total would otherwise drag the trend down. It is still plotted as an actual.
• The trend indicator (rising / falling / flat) compares the slope's effect over the horizon against the noise level, so small fluctuations read as flat.

The implementation lives in src/lib/forecast.ts.

Usage reports

The app accepts GitHub usage report CSVs (summarized, detailed, and AI usage reports). For the report types, columns, and how to download them, see the official docs: GitHub billing reports reference.

The parser tolerates missing optional columns and legacy column names across all report variants.

Getting started

npm install
npm run dev

Then open http://localhost:3000 and load a usage report CSV.

Contributing

See CONTRIBUTING.md for the tech stack, architecture and conventions, local setup, and development guidelines - including the hard client-only data privacy constraint.