Rig vs Preference Model

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Rig compared with Preference Model
Detail	Rig	Preference Model
What it does	Local development process manager for Mac and Windows with live logs, ports, and MCP integration for AI editors	Open-sourcing Karotte, a framework for building robust reinforcement learning environments
Stage	Not given	Not given
Founded	Not given	Not given
Based in	GB	Not given
Pricing model	Freemium	Open source
Pricing	Hobby: free forever (1 Mesh machine, 5 GB relay per month). Pro: $15/month or $144/year (5 machines, 25 GB relay). Team: $25/seat/month or $240/year (10 machines per seat pooled, 75 GB relay per seat pooled). Agency: $149/month or $1,430/year with 3 seats included, extra seats $25/month. Extra machi	Not given
Key features	Unified live logs and port management Local MCP hub with 95 scoped tools for Cursor, Claude, Codex, Windsurf, and VS Code WordPress support with isolated MySQL and local HTTPS Live Link previews for sharing on LAN QR or branded links Multi-machine Continuity backups and encrypted cloud sync Laravel and Valet integration	Sandboxed execution with non-privileged user isolation Process termination before grading Suspicious file detection and rejection Out-of-memory and fork-bomb prevention Hardened through one million evaluation runs Red-team tested for reward hacking vulnerabilities Scalable environment execution Robust benchmark building support
What makes it different	One unified control plane replacing separate tools for process management, logging, and port binding Native MCP integration with AI editors eliminating the need for raw shell commands Works alongside existing tools like Laravel Herd, Valet, and WordPress	Provides secure defaults and primitives specifically designed to prevent reward hacking in RL training Hardened through real-world testing at scale with over one million evaluation runs Designed for training environments where defenses must withstand continuous optimization attempts Addresses AI alignment at the highest leverage point through better environment construction Built and tested internally by frontier AI research team
Free plan	Not given	Not given
Open source	Not given	Not given
Platforms	Not given	Not given
Public API	Not given	Not given
Community votes	0	0
DR (Domain Rating by Ahrefs)	37 +1 (up) since last check	29 +1 (up) since last check
Trust Flow (Majestic)	20	11
Citation Flow (Majestic)	9	24
Referring domains (Majestic)	29	98

4 details filled in for both companies.

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