Vecstore vs Imagga: We Tested Both Image Search APIs

I wanted to understand how Imagga's visual search compares to ours, so I signed up, got API keys, and tested both APIs against the same images. The two products turned out to be more different than I expected. TL;DR: Imagga's "visual search" is built on image categorization and tag matching. Vecstore uses vector embeddings for actual visual similarity. Vecstore is about 8x faster on search (300ms vs 2.5s), doesn't require a separate database, supports text-to-image search, and auto-indexes without manual training. Imagga is stronger at structured image tagging, color extraction, and background removal. How the Two Approaches Differ The biggest takeaway from testing wasn't speed. It was that the two APIs use fundamentally different approaches to image search. Imagga categorizes images into tags using WordNet taxonomy and then finds other images that share similar tags. When you search for a dog photo, it first categorizes it as border_collie.n.01 with 93.4% confidence, then finds other