Ollama vs TabbyML

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

Ollama (rank #122) holds a modest lead over TabbyML (rank #569) on the overall Smoower ecosystem score (54 vs 35). The gap of 19 points reflects composite signals across code, docs, community and reach.

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

Ollama carries 190,304 GitHub stars across 4 public repos, with 3 repositories active in the last 90 days and 241 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 Ollama's footprint is roughly 5.6x larger, which usually shows up in downstream signals like inbound issues and third party integrations.

Ollama is the stronger read for anyone weighting momentum. 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.