AWS Cost Efficiency

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Amazon Rekognition Pricing and Optimization

Amazon Rekognition Cost Optimization Guide

Sanika Kotgire

published on

April 22, 2025

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Did you know?

Rekognition can detect thousands of different objects and scenes in images and videos, enabling applications beyond just facial recognition, such as inventory management and content categorization.

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Amazon Rekognition is a powerful AI service from AWS that enables developers to analyze images and videos using pre-trained and customizable machine learning models. From facial recognition and object detection to content moderation and custom labels, Rekognition provides a wide range of capabilities to build intelligent applications. However, understanding the pricing structure is essential to effectively manage costs.

In this blog, we’ll break down Amazon Rekognition’s pricing across different services and explore practical strategies to optimize your usage and reduce expenses.

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Amazon Rekognition Pricing Overview

Amazon Rekognition offers a range of powerful image and video analysis features through its various APIs, including facial recognition, label detection, content moderation, and more. Each feature is priced based on the specific service and usage, such as per image, per minute of video, or per hour of model training.

The table below provides a comprehensive overview of the key Rekognition services, their associated APIs, and pricing details to help users estimate costs and choose the right features for their use case.

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Feature	Service/API	Description	Price Range
Amazon Rekognition Image	AssociateFaces, CompareFaces, DisassociateFaces, IndexFaces, SearchFacesbyImage, SearchFaces, SearchUsersByImage, SearchUsers ,DetectFaces, DetectModerationLabels, DetectLabels, DetectText, RecognizeCelebrities, DetectPPE ,Detect image brightness, sharpness, etc.	Face detection and matching, object and scene recognition, content moderation, celebrity identification, and image properties analysis.	$0.001 – $0.0001875 per image
Amazon Rekognition Video	Content moderation, Label recognition, Text detection, Facial recognition, Celebrity recognition, Search for faces, Tracking people	Real-time or stored video stream analysis	$0.00817 – $0.10 per minute
Amazon Rekognition Custom Labels	Inference, Training	Train and run ML models on custom image data	$1.00 – $4.00 per hour
Amazon Rekognition Face Liveness	Face Liveness	Biometric check to verify user is physically present	$0.015 – $0.010 per test
Amazon Rekognition Custom Moderation	Training & Inference	Train and run custom moderation models	$0.00096 – $0.0003 per image

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Amazon Rekognition Pricing Calculation

ShopVista, a cloud-native e-commerce platform hosted on AWS, allows sellers to upload product images and short promotional videos to enhance their listings. To automate content classification and reduce manual oversight, the company integrates Amazon Rekognition into its media processing pipeline.

Rekognition is used for two primary tasks: First, the DetectLabels API is invoked to identify objects, scenes, and concepts in product images, such as clothing, electronics, and backgrounds. Second, the StartFaceDetection API is used to analyze promotional videos, ensuring the presence of human faces for branding and quality control.

All images and videos are stored in Amazon S3, and an AWS Lambda function is triggered upon each upload to initiate the appropriate Rekognition API. The solution is deployed in the US East (N. Virginia) region.

Cost Calculations

Image Analysis using DetectLabels

Total images analyzed per month: 1,000,000
First 1,000 images are free (Rekognition free tier)
Billable images: 1,000,000 - 1,000 = 999,000 images
Rekognition charges $1.00 per 1,000 images beyond the free tier
999,000 ÷ 1,000 = 999 units
999 units × $1.00 = $999.00
Total cost for image analysis: $999.00/month

Video Face Detection using StartFaceDetection

Total video analyzed per month: 10,000 minutes
Rekognition charges $0.10 per minute for face detection in stored videos
10,000 × $0.10 = $1,000.00
Total cost for video face detection: $1,000.00/month

Total Monthly Cost

$999.00 (image analysis) + $1,000.00 (video analysis) = Total: $1,999.00 per month

ShopVista incurs a monthly cost of $1,999 by analyzing 1 million product images and 10,000 minutes of promotional video using Rekognition’s DetectLabels and StartFaceDetection APIs.

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Strategies to Reduce Amazon Rekognition Costs

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1. Use Bounding Boxes to Reduce Image Size

Amazon Rekognition processes an image as a whole, and pricing depends on the image size and its complexity. When an image contains unnecessary background content such as wide landscapes, busy environments, or irrelevant objects , the service performs more computation, potentially reducing accuracy and increasing cost.

To mitigate this, use bounding boxes to crop your images, focusing only on relevant sections before sending them to Rekognition. For example, if you're only interested in detecting personal protective equipment (PPE) on a worker’s head, you can crop the image around the head and shoulders. This technique is particularly useful when working with Rekognition Custom Labels, where both training and inference costs depend on the volume and size of the data being processed.

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2. Use Rekognition Custom Labels Only When Necessary

Amazon Rekognition offers a range of built-in capabilities like DetectLabels, DetectFaces, DetectText, RecognizeCelebrities, and more. However, for more domain-specific use cases—such as identifying custom logos, animal breeds, or factory equipment—you can train your own models using Rekognition Custom Labels.

While powerful, Custom Labels are significantly more expensive, with charges for both training and ongoing inference:

Training: Starts at $4 per hour
Inference: Starts at $1 per hour

Therefore, you should only use Custom Labels when built-in APIs cannot fulfill your use case. For example, if you're building a content moderation system, first try using the DetectModerationLabels API. If that’s insufficient, only then proceed to train a custom moderation model.

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3. Use Asynchronous Video Analysis

Amazon Rekognition supports both real-time video streaming analysis and asynchronous batch video analysis. Real-time analysis (e.g., via Kinesis Video Streams) is ideal for applications like surveillance, access control, or live anomaly detection. However, it is more expensive, as compute resources are provisioned continuously to analyze the incoming video in real time.

In contrast, asynchronous video analysis allows you to:

Upload pre-recorded videos.
Trigger APIs like StartLabelDetection, StartFaceSearch, or StartPersonTracking.
Process video frames in the background.
Retrieve results later using completion callbacks or polling.

This asynchronous approach significantly reduces costs, especially if you don’t need real-time results or if the analysis is scheduled at off-peak hours.

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4. Selecting the appropriate API

Amazon Rekognition provides both Image and Video APIs to support various computer vision tasks such as content moderation, label detection, face recognition, and more. Selecting the appropriate API based on the nature of your workload can significantly improve cost efficiency. The Image API charges are based on the number of images processed, whereas the Video API charges per minute of video analyzed.

To understand the cost implications, consider the task of moderating a 60-second video. If you opt for the Video API, Amazon Rekognition processes the entire video at a flat rate, typically around $0.10 per minute. On the other hand, if you choose to extract frames from the video—say, two frames per second—you'll end up processing 120 images. With the Image API priced around $0.001 per image, this approach would cost approximately $0.12. In this scenario, the Video API proves to be more cost-effective.

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Conclusion

Amazon Rekognition offers powerful capabilities for image and video analysis, but its diverse pricing models require thoughtful planning to avoid unnecessary costs. In this blog, we examined how a company like ShopVista leverages Rekognition for product classification and branding with predictable monthly costs. We also explored multiple optimization strategies—ranging from bounding box cropping to asynchronous processing and API selection—that help manage compute and inference expenses effectively.

By understanding the nuances of Rekognition’s pricing model and aligning it with your actual use case, businesses can scale intelligent applications without incurring excessive charges. Cost-aware architecture and workload profiling are essential to maximize the value from Rekognition while maintaining operational efficiency on AWS.

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