We make it unbelievably easy to manage your team's payroll, benefits, computers, and apps.
Iterable vs Rippling
Developer ecosystem comparison across GitHub activity, SDKs, documentation, community, reach and momentum.
Iterable and Rippling (We make it unbelievably easy to manage your team's payroll, benefits, computers, and apps.) both appear on the Smoower Developer Ecosystem Index.
Iterable (rank #317) holds a narrow lead over Rippling (rank #455) on the overall Smoower ecosystem score (44 vs 39). The gap of 5 points reflects composite signals across code, docs, community and reach.
On code quality (the state of repositories, tests, releases and polish), Iterable is ahead of Rippling. On education (docs, guides and learning material for developers), Iterable is slightly ahead of Rippling. On community (issue response, PR reviews and discussion health), Iterable is slightly ahead of Rippling. On reach (how visible the ecosystem is beyond its own repos), Rippling is clearly ahead of Iterable. On momentum (release cadence and how fast the ecosystem moves), Iterable is clearly ahead of Rippling.
Iterable carries 269 GitHub stars across 100 public repos, with 16 repositories active in the last 90 days and 10 external contributors on record. Rippling shows 156 stars across 11 public repos, 1 active in the last 90 days and 11 external contributors.
Iterable is the stronger read for anyone weighting momentum. Rippling looks better where reach is the deciding factor. The table below breaks the scores down pillar by pillar; the linked profiles cover the underlying repos, docs and community signals in full.
Side-by-side metrics
| Metric | Iterable | Rippling |
|---|---|---|
| Ranking | ||
| Overall rank | #317 | #455 |
| Pillars | ||
| Overall | 44 | 39 |
| Code | 51 | 40 |
| Education | 59 | 56 |
| Community | 35 | 27 |
| Reach | 16 | 58 |
| Momentum | 58 | 6 |
| Builder experience | 0 | 0 |
| Signals | ||
| Stars | 269 | 156 |
| Forks | 212 | 126 |
| Public repos | 100 | 11 |
| Active repos (90d) | 16 | 1 |
| External contributors | 10 | 11 |
| Avg polish | 53 | 42 |
| Avg AI-readiness | 34 | 26 |