AssemblyAI vs TabbyML

AssemblyAI (Advanced Speech-to-Text) and TabbyML both appear on the Smoower Developer Ecosystem Index.

AssemblyAI (rank #401) holds a narrow lead over TabbyML (rank #569) on the overall Smoower ecosystem score (41 vs 35). The gap of 6 points reflects composite signals across code, docs, community and reach.

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

AssemblyAI carries 1,025 GitHub stars across 67 public repos, with 8 repositories active in the last 90 days and 11 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 33.2x larger, which usually shows up in downstream signals like inbound issues and third party integrations.

AssemblyAI is the stronger read for anyone weighting education. TabbyML 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.