# Radford alternatives: what AI models name instead, October 2026

HR AI Recommendation Index, October 2026 Edition. 224 of the 252 answers in the 1 category where Radford holds a standing named it neither first nor as an alternative; these are the first choices those answers made. Page: https://hr-ai-index.com/vendors/radford/alternatives/

## Named instead, most often (every category and segment added)

- Pave: 36
- Payscale: 21
- Mercer Total Remuneration Survey: 18
- Comprehensive.io: 10
- SalaryCube: 9
- Salary.com CompAnalyst: 7

## Compensation benchmarking data

**Small business**: 82 of 84 answers did not name Radford (Radford is #9 of 9 at 0%).

- Pave: first choice in 21 of those 82
- Payscale: first choice in 9 of those 82
- Comprehensive.io: first choice in 6 of those 82
- Pave Market Data Lite: first choice in 3 of those 82
- Salary.com CompAnalyst: first choice in 3 of those 82
- Bureau of Labor Statistics: first choice in 2 of those 82
- Databox: first choice in 2 of those 82
- OpenComp: first choice in 2 of those 82

**Mid-market**: 77 of 84 answers did not name Radford (Radford is #7 of 13 at 5%).

- Pave: first choice in 11 of those 77
- Payscale: first choice in 8 of those 77
- SalaryCube: first choice in 6 of those 77
- Mercer Total Remuneration Survey: first choice in 5 of those 77
- Ravio: first choice in 5 of those 77
- Comprehensive.io: first choice in 4 of those 77
- Salary.com CompAnalyst: first choice in 4 of those 77
- CompData Surveys: first choice in 2 of those 77

**Enterprise**: 65 of 84 answers did not name Radford (Radford is #2 of 12 at 16%).

- Mercer Total Remuneration Survey: first choice in 13 of those 65
- Pave: first choice in 4 of those 65
- Payscale: first choice in 4 of those 65
- Aon's Radford McLagan Compensation Database: first choice in 3 of those 65
- Deloitte: first choice in 3 of those 65
- SalaryCube: first choice in 3 of those 65
- Gartner: first choice in 2 of those 65
- Pave Market Data Pro: first choice in 2 of those 65

Published under CC BY 4.0. The output is the models' output; nothing here is a recommendation by the index.