Deepgram vs TabbyML

Deepgram and TabbyML both appear on the Smoower Developer Ecosystem Index.

Deepgram (rank #63) holds a meaningful lead over TabbyML (rank #569) on the overall Smoower ecosystem score (59 vs 35). The gap of 24 points reflects composite signals across code, docs, community and reach.

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

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. TabbyML shows 34,010 stars across 18 public repos, 5 active in the last 90 days and 12 external contributors. The star gap on its own does not decide the comparison, but TabbyML's footprint is roughly 13.4x larger, which usually shows up in downstream signals like inbound issues and third party integrations.

Deepgram is the stronger read for anyone weighting reach. TabbyML makes more sense for teams already using its adjacent tools. The table below breaks the scores down pillar by pillar; the linked profiles cover the underlying repos, docs and community signals in full.