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LandingLens vs SentiSight.ai

Both stand on ai image recognition software. Which one you can start free with, which one lets you read the code, and what each costs once you pay.

LandingLens

Free plan APISelf-hostedWeb+1

LandingLens brings image labeling, model training, and several deployment options into one computer vision workflow. Check the Free plan’s credit, download, and noncommercial-use limits against your project needs.

Visit LandingLens

SentiSight.ai

Free plan AndroidAPIiPhone+3

SentiSight.ai combines image annotation, model development, search and deployment options for a range of image tasks. Mobile apps are limited to predictions, so training and labeling require another supported route.

Visit SentiSight.ai

The facts

LandingLensSentiSight.ai
Free planYesYes
Open sourceNot publishedNot published
Runs onAPI, Self-hosted, Web, WindowsAndroid, API, iPhone, Linux, Self-hosted, Web
Lowest paid planFree or on requestFree or on request
Plans published22
MakerNot publishedNot published
Websitelanding.aisentisight.ai
On ai image recognition software#9 of 37#13 of 37

LandingLens: what it does well

  • Build models through image uploading, labeling, and training
  • Label book supports consistent class definitions
  • Multiple users can label collaboratively
  • Cloud, edge, Windows App, and API deployment

What to know first

  • Free plan stops training and inference at 1,000 monthly credits
  • Free-plan model downloads limited to one active project
  • Free-plan use is noncommercial
  • Enterprise pricing requires contacting sales

SentiSight.ai: what it does well

  • Supports classification, detection and segmentation models
  • Multiple annotation shapes and label types
  • Shared projects include manager progress and role controls
  • Online, REST API and offline deployment options
  • Pre-trained models cover several image-analysis tasks

What to know first

  • Mobile apps cannot train new models
  • Mobile apps cannot label existing uploads
  • Mobile labeling supports classification, not object labels

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