ggml vs Weaviate

ggml (AI inference at the edge) and Weaviate (Weaviate creates database software like the Weaviate vector search engine) both appear on the Smoower Developer Ecosystem Index.

ggml (rank #103) holds a narrow lead over Weaviate (rank #152) on the overall Smoower ecosystem score (55 vs 52). The gap of 3 points reflects composite signals across code, docs, community and reach.

On code quality (the state of repositories, tests, releases and polish), Weaviate is slightly ahead of ggml. On education (docs, guides and learning material for developers), Weaviate is slightly ahead of ggml. On community (issue response, PR reviews and discussion health), ggml is clearly ahead of Weaviate. On reach (how visible the ecosystem is beyond its own repos), ggml is clearly ahead of Weaviate. On momentum (release cadence and how fast the ecosystem moves), Weaviate is clearly ahead of ggml.

ggml carries 191,415 GitHub stars across 22 public repos, with 15 repositories active in the last 90 days and 308 external contributors on record. Weaviate shows 30,251 stars across 140 public repos, 32 active in the last 90 days and 51 external contributors. The star gap on its own does not decide the comparison, but ggml's footprint is roughly 6.3x larger, which usually shows up in downstream signals like inbound issues and third party integrations.

ggml is the stronger read for anyone weighting reach. Weaviate looks better where momentum 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.