Vector Database for Enterprise-grade AI and LLM applications
sgl-project vs Zilliz
Developer ecosystem comparison across GitHub activity, SDKs, documentation, community, reach and momentum.
sgl-project and Zilliz (Vector Database for Enterprise-grade AI and LLM applications) both appear on the Smoower Developer Ecosystem Index.
Zilliz (rank #122) holds a narrow lead over sgl-project (rank #278) on the overall Smoower ecosystem score (54 vs 46). The gap of 8 points reflects composite signals across code, docs, community and reach.
On code quality (the state of repositories, tests, releases and polish), Zilliz is ahead of sgl-project. On education (docs, guides and learning material for developers), Zilliz is ahead of sgl-project. On community (issue response, PR reviews and discussion health), sgl-project is clearly ahead of Zilliz. On reach (how visible the ecosystem is beyond its own repos), Zilliz is clearly ahead of sgl-project. On momentum (release cadence and how fast the ecosystem moves), Zilliz is ahead of sgl-project.
sgl-project carries 38,333 GitHub stars across 26 public repos, with 20 repositories active in the last 90 days and 349 external contributors on record. Zilliz shows 37,860 stars across 71 public repos, 29 active in the last 90 days and 105 external contributors.
sgl-project is the stronger read for anyone weighting community. Zilliz 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 | sgl-project | Zilliz |
|---|---|---|
| Ranking | ||
| Overall rank | #278 | #122 |
| Pillars | ||
| Overall | 46 | 54 |
| Code | 55 | 67 |
| Education | 74 | 83 |
| Community | 80 | 51 |
| Reach | 8 | 29 |
| Momentum | 40 | 50 |
| Builder experience | 0 | 0 |
| Signals | ||
| Stars | 38,333 | 37,860 |
| Forks | 8,904 | 3,995 |
| Public repos | 26 | 71 |
| Active repos (90d) | 20 | 29 |
| External contributors | 349 | 105 |
| Avg polish | 55 | 64 |
| Avg AI-readiness | 47 | 59 |