ProfitCtl

Unit economics as code for AI-native teams.
Compare pricing, infrastructure, and AI spend before it ships.

Install · Run an example · Documentation · Benchmark scenarios · Contribute

What ProfitCtl does

• Compare decisions before commitment. Evaluate pricing, infrastructure, model, API, and contract choices side by side.
• Model recurring-margin risk. Simulate growth and stress, then test explicit profitability covenants.
• Make cost assumptions inspectable. Track fixed and variable costs, payment fees, calibration inputs, and source provenance.
• Give agents grounded inputs. Use assess and the bundled cost-aware skill when a codebase needs a starter cost model.

Quick start

Run the committed hybrid comparison locally:

git clone https://github.com/IntelIP/ProfitCtl.git
cd ProfitCtl
go run . compare examples/hybrid_steady_profit.yml examples/hybrid_profit.yml

You will see revenue, recurring revenue, fees, booked and operating margins, cost per user, covenant results, and the leaders for each metric. For the signed release binary and Bun package, see the Install Guide. Pin the evaluator prerelease v0.4.0-rc.1; GitHub's latest-release endpoint selects stable releases.

Use it where decisions happen

Surface	Start here
Local CLI	simulate, compare, and validate
Cost-aware agent decisions	ProfitCtl cost-aware skill
CI checks	Cost model standards
Scenario examples	Benchmark scenarios

Production economics planning

Inspect service evidence, identify missing measurements, and compare explicitly modeled downstream effects with the new local system commands. Start with the Condere walkthrough and system design. Synthetic examples are not production cost forecasts.

Learn more

• Documentation index
• How ProfitCtl works
• Architecture
• Cost intelligence system design
• Open-source company plan
• ProfitCtl plugin pilot
• Website source
• Release downloads

Trust and participation

ProfitCtl is MIT licensed. See LICENSE, NOTICE, Trademark Guidelines, Security, and Contributing.