Deepgram vs ggml

Deepgram and ggml (AI inference at the edge) both appear on the Smoower Developer Ecosystem Index.

Deepgram (rank #63) holds a narrow lead over ggml (rank #103) on the overall Smoower ecosystem score (59 vs 55). The gap of 4 points reflects composite signals across code, docs, community and reach.

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

Deepgram carries 2,538 GitHub stars across 115 public repos, with 47 repositories active in the last 90 days and 30 external contributors on record. ggml shows 191,415 stars across 22 public repos, 15 active in the last 90 days and 308 external contributors. The star gap on its own does not decide the comparison, but ggml's footprint is roughly 75.4x larger, which usually shows up in downstream signals like inbound issues and third party integrations.

Deepgram is the stronger read for anyone weighting code quality. ggml looks better where community 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.