oss-savings 0.1.1

Calculate how much money open source saved you

OSS Savings Calculator

How much money did open source save you?

A command-line tool that estimates the development cost you avoided by using an open-source library instead of building it yourself.

$ oss-savings pytorch/pytorch

  Build savings:            71,189 hrs     $12.46M
  Maintenance savings:     124,581 hrs     $21.80M   (over 7 years)
  ─────────────────────────────────────────────────
  TOTAL:                   195,770 hrs     $34.26M

The total represents what it would cost to build from scratch plus maintain for the usage period (default: min of repo age or 7 years).

Why?

Every time you type pip install or npm install, you're importing decades of collective engineering effort. But how much effort, exactly?

This tool turns that vague intuition into concrete numbers—useful for:

• Justifying OSS contributions to your manager ("We use $50M of free software, maybe we should give back?")
• Appreciating maintainers ("Your library saved our company $2M")
• Understanding technical debt ("We depend on a $500K library maintained by one person")
• Due diligence ("How much would it cost to replace this dependency?")

Installation

pip install oss-savings

Or install from source:

git clone https://github.com/skyfallsin/oss-savings
cd oss-savings
pip install -e .

No external dependencies. Just Python 3.10+.

Usage

Analyze a single repository

oss-savings facebook/react
oss-savings https://github.com/rails/rails

# Include dependency analysis
oss-savings expressjs/express --deps

# Use industry-standard COCOMO model (higher estimates)
oss-savings pytorch/pytorch --model cocomo

# Customize assumptions
oss-savings django/django --hourly-rate 150 --years-of-use 5

Analyze your entire dependency file

# Analyze all your dependencies at once
oss-analyze package.json
oss-analyze requirements.txt
oss-analyze Gemfile
oss-analyze Cargo.toml
oss-analyze go.mod

# Limit to first N deps (useful for testing / rate limits)
oss-analyze package.json --limit 20

This gives you:

• Total savings across all dependencies
• Top dependencies by value
• Funding recommendations (high value + high risk = fund this!)
• Risk warnings for dependencies with bus factor or staleness issues

Sample Results

Repository	Category	Build	Maintenance	Total
pytorch/pytorch	AI/ML	$12.5M	$21.8M	$34.3M
rails/rails	Framework	$4.2M	$18.6M	$22.8M
pallets/flask	Framework	$2.1M	$651K	$2.75M
sindresorhus/is	Utility	$73K	$97K	$170K

How It Works

The calculator fetches repository stats from GitHub and applies a multi-factor model:

1. Lines of Code Estimation

• Per-language bytes-to-LOC conversion (C averages 25 bytes/line, Python 34, etc.)
• Filters out docs, config, and stylesheets
• Estimates test/example code ratio from topics and description
• Applies diminishing returns for mega-repos (you don't use all of Linux)

2. Complexity Multipliers

Factor	Range	Based On
Domain	1.0-1.1x	AI/ML, crypto, compilers vs utilities
Performance	1.0-1.2x	C/C++/Rust/Go/CUDA ratio
Safety-critical	1.0-1.4x	Security, crypto, database, infra keywords
Contributor overhead	1.0-1.5x	Coordination cost saved (log scale)

3. Build vs Maintenance

• Build: LOC ÷ productivity × complexity × hourly rate
• Maintenance: 15-25% of build effort per year, scaled by commit activity
• COCOMO mode: Uses effort = a × KLOC^b for superlinear scaling

4. Risk Assessment

Every dependency is a liability. The tool scores risk (0-100) based on:

• Staleness (last commit)
• Bus factor (contribution concentration)
• Issue backlog
• Archived/disabled status
• Popularity (low stars = low visibility)

The Math

Linear model (default):

build_hours = (effective_loc / loc_per_hour) × complexity
maintenance_hours = build_hours × 0.15-0.25 × years × activity_factor
total_cost = (build_hours + maintenance_hours) × $175/hr

COCOMO model (--model cocomo):

person_months = a × (KLOC ^ b) × complexity
build_hours = person_months × 152

Where a and b vary by domain (2.4-3.0 and 1.03-1.10 respectively).

What is COCOMO?

COCOMO (Constructive Cost Model) is an algorithmic software cost estimation model developed by Barry Boehm in 1981. It's one of the most well-known and studied models in software engineering economics.

The key insight is that effort scales superlinearly with code size—a 100K LOC project takes more than 10x the effort of a 10K LOC project due to increased complexity, communication overhead, and integration challenges.

COCOMO II (1995) refined the model with modern software practices. Our "COCOMO-lite" implementation uses the basic formula with domain-specific parameters, providing more realistic estimates for large codebases than simple LOC/hour calculations.

Limitations

This is a rough estimate, not an audit. The methodology is opinionated—there are many valid ways to calculate software costs, and reasonable people disagree on the right approach. If you have a better model, send a PR!

The tool:

• Can't know how much of a library you actually use
• Uses heuristics for test/doc ratio, not actual file analysis
• Doesn't account for your team's specific productivity
• Ignores integration/learning costs (you still pay those)
• GitHub API has rate limits (see below for authentication)

The goal is order-of-magnitude accuracy. If it says $10M, the true value is probably $3M-$30M, not $100K or $100M.

Philosophy

Open source is infrastructure. Like roads and bridges, we use it without thinking about what it cost to build. Unlike roads, there's no tax funding it—just mass unpaid labor.

Consider sponsoring a maintainer whose work you depend on.

GitHub Rate Limits

Without authentication, GitHub limits you to 60 API requests/hour. With a token, you get 5,000/hour.

To authenticate using the GitHub CLI:

gh auth login
export GITHUB_TOKEN=$(gh auth token)

Add the export to ~/.zshrc or ~/.bashrc for persistence.

The tool automatically uses GITHUB_TOKEN or GH_TOKEN environment variables if set.

CLI Options

positional arguments:
  repo                  GitHub repo (owner/repo or full URL)

options:
  --deps                Analyze dependencies (npm, pypi, rubygems, crates)
  --years-of-use N      Years you'll rely on this library (default: min(repo age, 7))
  --hourly-rate N       Loaded hourly rate in USD (default: 175)
  --model {linear,cocomo}
                        Estimation model (default: linear)

Contributing

PRs welcome. Ideas for improvement:

• GitHub token support for higher rate limits
• --deep-loc mode that analyzes the file tree
• Usage coverage estimation from import analysis
• Integration cost modeling
• Export to JSON/CSV
• Web interface

License

MIT

AI use

Yes, this project was coded in an afternoon with LLM assistance.