
AWS Rekognition
AWS's deep learning API for image and video analysis, face recognition, and content moderation
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About AWS Rekognition
Amazon Rekognition is AWS's fully managed computer vision service that adds image and video analysis to applications through simple API calls, without requiring any machine learning expertise or infrastructure to build and train models from scratch. It analyzes millions of images and hours of video within seconds, making deep learning-based visual analysis accessible to developers who just want to call an API rather than build a computer vision pipeline.
The service covers a wide range of use cases through distinct capabilities: face detection and analysis (identifying attributes like emotion, age range, and whether eyes are open), face comparison and search against a private collection, Face Liveness to detect spoofing attempts during remote identity verification, label detection for objects and scenes, text detection in images, celebrity recognition, and content moderation that flags unsafe or inappropriate visual content. For teams with specialized needs, Rekognition Custom Labels uses AutoML to train a model that recognizes business-specific objects, such as a company logo or a piece of equipment on a factory line, using as few as 10 to 20 training images. Amazon Rekognition Video extends these capabilities to both stored video in Amazon S3 and real-time streaming video from connected cameras via Amazon Kinesis Video Streams.
Rekognition is built for developers and businesses already working within or adopting the AWS ecosystem: security and identity teams building onboarding verification flows, media and advertising companies automating content tagging and moderation, retailers cataloging product imagery, and connected-device makers building smart home or workplace safety alerts. It is not a no-code tool or a consumer app — it is an API-first service that expects integration through the AWS SDKs, CLI, or console, and pricing scales with usage rather than following flat subscription tiers.
Key Features
- Pretrained image and video analysis APIs requiring no machine learning expertise
- Face detection, analysis, comparison, and search against a private face collection
- Face Liveness detection to prevent spoofing during identity verification
- Custom Labels AutoML training to detect brand logos and business-specific objects from as few as 10 images
- Automatic content moderation to flag unsafe or inappropriate images and video
- Text detection and extraction from images, including skewed or distorted text
- Celebrity recognition for media, marketing, and advertising cataloging
- Real-time streaming video analysis via Amazon Kinesis Video Streams for smart alerts
Pros
- Pay-as-you-go pricing with no minimum fees or upfront commitment, and a genuinely useful 12-month free tier
- No machine learning expertise required to add production-grade computer vision to an application
- Broad feature set covering faces, labels, text, moderation, and custom object detection in one service
- Custom Labels can train a usable model from as few as 10 to 20 images via AutoML
- Deep integration with the AWS ecosystem, including S3, Kinesis Video Streams, and Lambda
- Scales automatically from small projects to millions of images or hours of video per month
Cons
- Pricing can scale quickly for high-volume use cases (millions of images or hours of video per month), requiring careful cost modeling
- Requires an AWS account and familiarity with the AWS console, IAM permissions, and SDKs, which adds setup overhead for non-AWS users
- Face recognition and identity verification features raise privacy and compliance considerations, especially for biometric data in regulated regions
- Custom Labels training and inference are billed hourly even when idle unless resources are manually deprovisioned
- No built-in low-code interface for non-developers — it is API-first and expects a technical integration
Pricing
Amazon Rekognition uses pay-as-you-go pricing with no upfront commitment across four usage categories. Image analysis: Group 1 APIs (face search/compare/index) and Group 2 APIs (labels, moderation, text, celebrities) are billed per image on a tiered scale starting at $0.0010 per image for the first million images per month, dropping to $0.0004 per image at higher volumes; Image Properties is billed separately starting at $0.00075 per image. Face metadata storage costs $0.00001 per face or user vector per month. Video analysis: stored video is billed per minute (for example $0.10/min for Label Detection, $0.05/min for Shot Detection), while streaming video events cost around $0.00817 per minute processed. Custom Labels charges $1 per training hour and $4 per inference hour (inference must be manually deprovisioned to stop billing). Face Liveness checks start at $0.015 per check for the first 500,000 checks per month, decreasing at higher volumes. Custom Moderation adds $5 per training hour plus a tiered per-image inference cost starting at $0.0012 per image. New AWS accounts get a 12-month Free Tier (1,000 images/month, 60 video minutes/month, 2 free training hours) plus up to $200 in AWS Free Tier credits.
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Frequently Asked Questions
Amazon Rekognition offers a generous AWS Free Tier for 12 months: 1,000 images per month across its Group 1 and Group 2 image APIs, 1,000 free face metadata objects stored, and 60 free minutes of video analysis per month. Custom Labels includes 2 free training hours and 1 free inference hour per month during the free tier period.
Amazon Rekognition uses pay-as-you-go, tiered pricing with no upfront commitment. Image analysis starts around $0.001 per image for the first million images per month and drops at higher volumes. Stored video analysis is priced per minute (for example, $0.10 per minute for label detection), and streaming video events are billed per minute processed. Custom Labels charges separately for training hours and inference hours.
Yes. Amazon Rekognition Custom Labels lets you train a custom computer vision model with as few as 10 to 20 labeled images using AutoML, without needing to write machine learning code or manage training infrastructure yourself.
Amazon Rekognition Face Liveness is a dedicated feature that analyzes a short selfie video to detect spoofing attempts, including printed photos, digital replays, deepfakes, and 3D masks, making it suitable for remote identity verification during onboarding or authentication flows.
Yes. Amazon Rekognition Video can process both stored video from Amazon S3 and real-time streaming video from Amazon Kinesis Video Streams, supporting use cases like content moderation on uploaded footage as well as live alerts from security or connected-home cameras.
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