Computer Vision
Amazon Rekognition
Managed image and video analysis on AWS
Paid - from Pay as you goRuns in the cloud
Pricing
Paid - from Pay as you go
Pay as you go
Platforms
Web - API
Cloud API
Category
Computer Vision
58 tools
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Last updated
Sep 23
Listing updated
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Moderate user-generated content
Screen uploads for explicit or violent imagery automatically and route violations to review queues before they ever go public.
Pricing checked Sep 2026
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About Amazon Rekognition
Rekognition is AWS's managed computer vision service. Its image APIs cover label and scene detection, text extraction, face detection, analysis, comparison, and search within stored collections, plus celebrity recognition and safety-equipment (PPE) detection. Custom Labels trains domain-specific models on your own examples without writing model code.
Video APIs extend the same ideas to stored and streaming footage: person and object tracking, face detection, text and celebrity detection, and content moderation, consumed through Kinesis Video Streams or batch jobs against S3. Everything is invoked through SDKs, the AWS CLI, or the console, with IAM policies for access control and Lambda for event-driven pipelines.
Rekognition fits teams already on AWS that want perception features without model operations. Costs follow usage per image or per minute of video, a free tier covers many features for the first year, and biometric use cases carry real privacy and legal obligations.
- Fully managed: no servers, models, or GPU fleets to run
- Deep integration with S3, Lambda, IAM, and Kinesis Video Streams
- Custom Labels trains domain-specific models without ML expertise
- Per-use pricing with a free tier covering many features for a year
- Value is tied to AWS; porting away means rebuilding integrations
- Niche or domain-specific imagery often needs Custom Labels to compete
- Face search creates biometric privacy obligations under laws like BIPA
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See categoryPrograms that detect, classify, and interpret visual data, from pretrained labeling APIs you can call this afternoon to annotation and training platforms for custom models. Listings flag deployment targets (cloud, edge, on-prem), accuracy baselines, and data-privacy posture.
