Gravio vs. Microsoft Azure IoT Edge

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Gravio
Score 0.0 out of 10
Mid-Size Companies (51-1,000 employees)
Gravio is an Edge Computing Platform that connects sensors and computer vision AI/ML. Gravio users can deploy the edge server called "Gravio HubKit" on their own macOS, Windows, or Linux hardware (including Raspberry Pi). Or they can use the "Gravio Hub", a set-top box that fully with LTE functionality, and the Gravio HubKit pre-installed. Gravio integrates with common standards, including HTTP…N/A
Azure IoT Edge
Score 8.0 out of 10
N/A
Azure IoT Edge is a fully managed service built on Azure IoT Hub. Users can deploy cloud workloads—artificial intelligence, Azure and third-party services, or business logic—to run on Internet of Things (IoT) edge devices via standard containers. The vendor states that by moving certain workloads to the edge of the network, devices spend less time communicating with the cloud, react more quickly to local changes, and operate reliably even in extended offline periods.N/A
Pricing
GravioMicrosoft Azure IoT Edge
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
GravioAzure IoT Edge
Free Trial
YesNo
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeOptionalNo setup fee
Additional DetailsThe free version does not include any hardware. The basic version can be ordered in the Mac and Windows App Store app. More advanced packages can be ordered via order form in the user portal.
More Pricing Information
Community Pulse
GravioMicrosoft Azure IoT Edge
Features
GravioMicrosoft Azure IoT Edge
Internet of Things
Comparison of Internet of Things features of Product A and Product B
Gravio
-
Ratings
Microsoft Azure IoT Edge
9.2
Ratings
14% above category average
IoT Device Management00 Ratings9.90 Ratings
Device Security00 Ratings9.90 Ratings
IoT Data Management00 Ratings10.00 Ratings
IoT Analytics00 Ratings8.00 Ratings
IoT Integration00 Ratings8.00 Ratings
User Ratings
GravioMicrosoft Azure IoT Edge
Likelihood to Recommend
-
(0 ratings)
8.0
(0 ratings)
User Testimonials
GravioMicrosoft Azure IoT Edge
Likelihood to Recommend
No answers on this topic
It is well suited if you have a production plant and you want to cut down the cost of cloud storage. It is best for medium to large companies if they want to process their machine generated data at their production site and only store the data that is required in cloud storage. It is less appropriate for small companies.
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Pros
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  • Innovative virtual learning environments that fasttracked our transition to remote learning at the height of the Covid pandemic in 2020
  • Mission critical business solutions for academic members of staff
  • Video conferencing and IoT devices connections
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Cons
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  • We had some trouble such as latency issues [and] connectivity while using Azure blob storage as an Edge Module on Azure IoT Edge runtime, so they should look into it.
  • We were stuck while defining the routes for different Edge Modules on Azure IoT Edge, so they should simplify it and improve the related documentation section as well.
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Alternatives Considered
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In terms of creating a smart digital workplace, in terms of being granular in enterprise security, provides a modernised experience, high quality data analysis at the edge of computing, while providing efficient mobility and workplace services.
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Return on Investment
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  • Very High ROI as Microsoft Azure IoT Edge completely enables the features of Edge computing.
  • Real-time analytics can be achieved with Microsoft Azure IoT Edge.
  • No Negative impact as such.
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ScreenShots

Gravio Screenshots

Screenshot of Data Management to organise Areas and Data LayersScreenshot of Create your own actions from an ever-growing number of action components; use the shell script component to execute any shell script based on sensor data.Screenshot of HTTP example component where you can send sensor data and values to a Rest/API endpointScreenshot of Trigger configuration to determine under which conditions an action should be executed; in this example, we use a camera feed and TensorFlow people counting model to trigger an action if more than 10 people are detected.