ggml vs Qdrant

ggml (AI inference at the edge) and Qdrant (Creating advanced vector search technology) both appear on the Smoower Developer Ecosystem Index.

Qdrant (rank #5) holds a modest lead over ggml (rank #103) on the overall Smoower ecosystem score (71 vs 55). The gap of 16 points reflects composite signals across code, docs, community and reach.

On code quality (the state of repositories, tests, releases and polish), Qdrant is clearly ahead of ggml. On education (docs, guides and learning material for developers), Qdrant is slightly ahead of ggml. On community (issue response, PR reviews and discussion health), ggml is clearly ahead of Qdrant. On reach (how visible the ecosystem is beyond its own repos), ggml is clearly ahead of Qdrant. On momentum (release cadence and how fast the ecosystem moves), Qdrant 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. Qdrant shows 44,913 stars across 132 public repos, 89 active in the last 90 days and 99 external contributors. The star gap on its own does not decide the comparison, but ggml's footprint is roughly 4.3x larger, which usually shows up in downstream signals like inbound issues and third party integrations.

ggml is the stronger read for anyone weighting reach. Qdrant 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.