AWS Panorama devices bring computer vision (CV) to your existing on-premises cameras. You purchase the physical device upfront, then are charged based on the number of unique camera video streams processed on the device each month. Additional charges apply for cloud storage of application assets deployed to the device, including machine learning (ML) models and business logic.
Pricing for AWS Panorama Devices and Usage
- Device: You pay a one-time cost up front for the device.
- Device usage: You are charged $8.33 per month for each unique camera video stream processed on the device.
- AWS Panorama cloud storage: AWS Panorama stores versioned copies of application assets deployed to devices (including ML models and business logic) in the cloud. You are charged $0.10 per GB, per month for this storage.
Summarized device pricing is below:
|AWS Panorama Devices||Device cost||Device usage|
|AWS Panorama Appliance||$4,000 per device||$8.33 per month per active camera stream|
|Lenovo ThinkEdge SE70||$2,339 MSRP* per device
||$8.33 per month per active camera stream|
*This price reflects the Manufacturer’s Suggested Retail Price (MSRP) of the device. Please contact your Lenovo sales representative for a quote.
You may incur additional charges if the business logic deployed to your AWS Panorama device uses other AWS services. For example, if your business logic uploads ML predictions to Amazon Simple Storage Service (Amazon S3) for offline analysis, you will be billed separately by Amazon S3 for any storage charges incurred. For details, visit the Amazon S3 pricing page or the pricing pages of other relevant AWS services.
In this example, you purchase an AWS Panorama Appliance to monitor wait times inside your quick service restaurant and in the drive-through. You train an ML model in Amazon SageMaker to identify how long the lines are throughout the day. You write the business logic that logs the total line length every 15 minutes. The total line length time is sent from the appliance to the business systems database on your local area network every 24 hours.
Your line length application assets, which include the ML model and business logic, are 200 MB in size. You run this application on your appliance against seven cameras streams continuously.
In this example, you want to better understand your supply chain and gather freight operations data at five transportation yards. You purchase one AWS Panorama Appliance for each site (five total), where there are 10 cameras already installed per site (50 cameras total). You use an existing ML model you previously trained in Amazon SageMaker to identify different objects, such as labels, barcodes, parts, and products. Your associated business logic sends results from the appliance to the site freight operations analytics system.
Your freight operations application assets, which include the ML model and the business logic, are 2 GB in size. You use AWS Panorama to run this freight operations model against all 10 camera streams for each site’s appliance (10 cameras x 5 appliances = a total of 50 camera streams).
In this example, you purchase an AWS Panorama-enabled Lenovo ThinkEdge SE70 device to monitor customer wait times at the checkout counter in a small convenience store. You train an ML model in Amazon SageMaker to identify how long the lines are at the checkout counter throughout the day. You write the business logic that logs the total line length every 15 minutes. The total line length time is sent from the appliance to the business systems database on your local area network every 24 hours.
Your line length application assets, which include the ML model and business logic, are 200 MB in size. You run this application on your device against seven cameras streams continuously.