Iterable vs Linear
Developer ecosystem comparison across GitHub activity, SDKs, documentation, community, reach and momentum.
Iterable and Linear (A purpose-built tool for planning and building products) both appear on the Smoower Developer Ecosystem Index.
Linear (rank #103) holds a modest lead over Iterable (rank #317) on the overall Smoower ecosystem score (55 vs 44). The gap of 11 points reflects composite signals across code, docs, community and reach.
On code quality (the state of repositories, tests, releases and polish), Iterable is slightly ahead of Linear. On education (docs, guides and learning material for developers), Iterable is slightly ahead of Linear. On community (issue response, PR reviews and discussion health), Linear is slightly ahead of Iterable. On reach (how visible the ecosystem is beyond its own repos), Linear is clearly ahead of Iterable. On momentum (release cadence and how fast the ecosystem moves), Iterable is ahead of Linear.
Iterable carries 269 GitHub stars across 100 public repos, with 16 repositories active in the last 90 days and 10 external contributors on record. Linear shows 1,971 stars across 22 public repos, 5 active in the last 90 days and 21 external contributors. The star gap on its own does not decide the comparison, but Linear's footprint is roughly 7.3x larger, which usually shows up in downstream signals like inbound issues and third party integrations.
Iterable is the stronger read for anyone weighting momentum. Linear 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 | Linear |
|---|---|---|
| Ranking | ||
| Overall rank | #317 | #103 |
| Pillars | ||
| Overall | 44 | 55 |
| Code | 51 | 45 |
| Education | 59 | 56 |
| Community | 35 | 36 |
| Reach | 16 | 76 |
| Momentum | 58 | 41 |
| Builder experience | 0 | 0 |
| Signals | ||
| Stars | 269 | 1,971 |
| Forks | 212 | 436 |
| Public repos | 100 | 22 |
| Active repos (90d) | 16 | 5 |
| External contributors | 10 | 21 |
| Avg polish | 53 | 42 |
| Avg AI-readiness | 34 | 31 |