ExpressVPN vs Heap
Developer ecosystem comparison across GitHub activity, SDKs, documentation, community, reach and momentum.
ExpressVPN and Heap (Product Insights) both appear on the Smoower Developer Ecosystem Index.
ExpressVPN and Heap sit at essentially the same overall ecosystem score (35), which is unusual and worth reading through the pillars below.
On code quality (the state of repositories, tests, releases and polish), ExpressVPN is clearly ahead of Heap. On education (docs, guides and learning material for developers), Heap is slightly ahead of ExpressVPN. On community (issue response, PR reviews and discussion health), ExpressVPN is ahead of Heap. On reach (how visible the ecosystem is beyond its own repos), ExpressVPN is ahead of Heap. On momentum (release cadence and how fast the ecosystem moves), ExpressVPN is slightly ahead of Heap.
ExpressVPN carries 1,250 GitHub stars across 12 public repos, with 3 repositories active in the last 90 days and 7 external contributors on record. Heap shows 215 stars across 65 public repos, 6 active in the last 90 days and 1 external contributors. The star gap on its own does not decide the comparison, but ExpressVPN's footprint is roughly 5.8x larger, which usually shows up in downstream signals like inbound issues and third party integrations.
ExpressVPN is the stronger read for anyone weighting code quality. Heap looks better where education 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 | ExpressVPN | Heap |
|---|---|---|
| Ranking | ||
| Overall rank | #569 | #569 |
| Pillars | ||
| Overall | 35 | 35 |
| Code | 57 | 35 |
| Education | 69 | 72 |
| Community | 30 | 14 |
| Reach | 48 | 30 |
| Momentum | 15 | 12 |
| Builder experience | 0 | 0 |
| Signals | ||
| Stars | 1,250 | 215 |
| Forks | 220 | 105 |
| Public repos | 12 | 65 |
| Active repos (90d) | 3 | 6 |
| External contributors | 7 | 1 |
| Avg polish | 63 | 37 |
| Avg AI-readiness | 29 | 23 |