One API call searches your entire image database by visual similarity. No tags, no metadata, no ML pipeline required.
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Navy henley
Searching by visual similarity

Cotton henley
100%

Slim-fit henley
94.3%

Ribbed henley
91.8%
1
API call
0
ML models
<180ms
response
<180ms
Search latency
99.9%
Uptime
Millions
of images searchable
Customer sees a product they like somewhere. They upload the photo, your store finds every similar item you carry. Better than keyword search because shoppers don't always know what something is called.
Millions of images, and users want "something like this one." Reverse image search lets them browse by visual similarity instead of guessing keywords. Better discovery means more licenses sold.
Catch reposted content, stolen images, or duplicate listings. Upload the image, find every copy or near-copy across your database. Works even when images are cropped, resized, or filtered.
A buyer sees a kitchen they love. They upload the photo and find every listing with a similar kitchen. Visual search surfaces properties that keyword filters miss entirely.
Building reverse image search from scratch means running a CLIP model for embeddings, a vector database for storage, and a sync pipeline to keep them connected. With Vecstore, you skip all of that.
Generate image embeddings
$2-10K/month for inference
Store and index embeddings
Keep source and vectors in sync
Estimated time to production
2-3 months
Embeddings generated automatically
Results with similarity scores
No GPUs, no models, no vector DB
Scale up or down, no commitment
Time to production
An afternoon
Reverse image search is one way to search your images. The same database also supports text-to-image search, face search, and OCR. One API key, one database.
Users type "red dress with floral pattern" and get matching images. No tags needed.
Learn more →Upload a face, find every match across your library. Works across angles and lighting.
Learn more →
1M+ searches powered by Vecstore this year
25 Free credits. No credit card required.