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Azure IoT Edge VS AWS Greengrass

Compare Azure IoT Edge VS AWS Greengrass and see what are their differences

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  2. » Videos
  3. » Reviews
  4. » Alternatives

Azure IoT Edge logo Azure IoT Edge

Connect cloud intelligence to your edge devices with Azure IoT Edge, a comprehensive service that deploys artificial intelligence and custom logic to all IoT devices.

AWS Greengrass logo AWS Greengrass

Local compute, messaging, data caching, and synch capabilities for connected devices
  • Azure IoT Edge Landing page
    Landing page //
    2023-02-11
  • AWS Greengrass Landing page
    Landing page //
    2023-03-28

Azure IoT Edge features and specs

  • Cloud Intelligence on Edge
    Azure IoT Edge allows you to bring cloud intelligence locally by running AI, analytics, and machine learning models on edge devices, enabling real-time decision-making and reduced latency.
  • Offline Capabilities
    IoT Edge can operate independently without continuous cloud connectivity, ensuring that operations continue in remote or intermittent connectivity environments.
  • Scalability
    It supports the seamless deployment and scaling of workloads across multiple devices, allowing businesses to expand their IoT infrastructure without substantial overhead.
  • Security
    Azure IoT Edge offers enterprise-grade security, providing features like device authentication and data encryption, enhancing the protection of edge devices and data.
  • Integration with Azure Services
    The platform integrates well with other Azure services, enabling users to leverage a wide range of tools for analytics, monitoring, and management in a cohesive ecosystem.

Possible disadvantages of Azure IoT Edge

  • Complexity
    Setting up and managing IoT Edge solutions can be complex and may require specialized knowledge and expertise, potentially leading to higher development and maintenance costs.
  • Resource Intensive
    Running certain workloads on edge devices can be resource-intensive, potentially requiring more powerful hardware and increasing the cost.
  • Dependency on Azure Ecosystem
    While beneficial in integration, relying heavily on the Azure ecosystem might limit flexibility or increase dependency on Microsoft's platform.
  • Initial Costs
    There may be significant initial costs involved in setting up and deploying IoT Edge infrastructure, which might be a barrier for small or budget-constrained organizations.
  • Updates and Management
    Managing updates and ensuring all edge devices are consistently synchronized and up to date can be challenging, especially in large deployments.

AWS Greengrass features and specs

  • Edge Computing
    AWS Greengrass allows devices to process data locally without relying on cloud resources, reducing latency and ensuring continued operation even with intermittent connectivity.
  • Seamless AWS Integration
    Seamlessly integrates with a variety of AWS services such as AWS Lambda, AWS IoT Core, and Amazon S3, allowing for enhanced functionality and simplified data exchange between edge devices and the cloud.
  • Security Features
    Offers robust security features, including data encryption for both in-transit and at-rest data, ensuring secure communication and data storage.
  • OTA Updates
    Provides over-the-air software updates, allowing developers to deploy new updates and patches to edge devices securely and efficiently.
  • Machine Learning at the Edge
    Supports ML inference capabilities, enabling machine learning models to run locally on devices, which is essential for real-time data processing and decision-making.

Possible disadvantages of AWS Greengrass

  • Complexity
    The integration of Greengrass in IoT solutions can add complexity, requiring a good understanding of both AWS services and edge computing.
  • Cost Considerations
    While processing data locally can reduce cloud costs, there may be additional expenses related to maintaining the hardware and ensuring compatibility with Greengrass, as well as costs associated with AWS usage.
  • Device Compatibility
    Not all devices may be compatible with AWS Greengrass, which may limit its use cases or require specific hardware configurations.
  • Dependency on AWS Ecosystem
    Being heavily integrated with the AWS ecosystem means that changes or outages in AWS services can potentially impact Greengrass deployments.
  • Learning Curve
    There may be a steep learning curve for developers who are new to AWS Greengrass, especially when it comes to deploying and managing complex IoT applications.

Azure IoT Edge videos

+ Add

Azure IoT Edge: architecture, demo on Raspberry Pi, pricing

AWS Greengrass videos

+ Add

Run ML Models at the Edge with AWS Greengrass ML

Category Popularity

0-100% (relative to Azure IoT Edge and AWS Greengrass)

Azure IoT Edge

AWS Greengrass

IoT Platform
37 37%
IoT Platform
63% 63
Data Dashboard
36 36%
Data Dashboard
64% 64
Analytics
30 30%
Analytics
70% 70
IoT
38 38%
IoT
62% 62

User comments

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Social recommendations and mentions

Based on our record, AWS Greengrass should be more popular than Azure IoT Edge. It has been mentiond 6 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Azure IoT Edge mentions (3)

  • Top Programming Languages for AI Development in 2025
    Connectivity to edge devices and cloud platforms. - Source: dev.to / 5 months ago
  • How to get the EK and Registration ID from a TPM 2.0 module on Raspian
    I am currently working on an IoT Project for my Bachelor's thesis. The goal is to gather data from an existing machine and send it to an Azure cloud via AMQP. To do this I have set up an IoT Hub and will be using the Azure IoT Edge runntime to connect and send the Data. For initial development, I have authenticated my devices to the cloud using symmetric keys generated by the IoT hub. Now I want to switch to... Source: almost 4 years ago
  • How to securely handle Data on Raspberry Pi-based device
    I get my data from an existing machine via Modbus RTU. For the cloud infrastructure, we are working with Microsoft Azure. I am using a Revolution Pi Flat as my edge device (basically an industrial-grade Pi) and plan to use the Azure IoT Edge runtime for added safety and easy deployments of containers from the cloud. There is just one problem. How do I handle data safely while it is on my device so a potential... Source: almost 4 years ago

AWS Greengrass mentions (6)

  • Key Features of AWS IoT Greengrass
    AWS IoT Greengrass is an open-source IoT edge runtime developed by Amazon Web Services. It allows connected devices to perform data processing, run applications, and communicate locally, even when internet connectivity is limited or unavailable. Once devices reconnect, the system synchronizes seamlessly with AWS cloud services. - Source: dev.to / 18 days ago
  • Orchestrating Application Workloads in Distributed Embedded Systems: Setting up a Nomad Cluster with AWS IoT Greengrass - Part 1
    In this blog post series, we will demonstrate how to use AWS IoT Greengrass and Hashicorp Nomad to seamlessly interface with multiple interconnected devices and orchestrate service deployments on them. Greengrass will allow us to view the cluster as a single "Thing" from the cloud perspective, while Nomad will serve as the primary cluster orchestration tool. - Source: dev.to / over 2 years ago
  • AWS Summit 2022 Australia and New Zealand - Day 2, AI/ML Edition
    AWS IoT Greengrass allows one to manage their IOT Edge devices, download ML models locally, so that inference can then be also be done locally. - Source: dev.to / over 3 years ago
  • Applying DevOps Principles to Robotics
    To assist in deployment and management of workloads in your fleet, it's worth taking advantage of a fleet or device management tool such as AWS GreenGrass, Formant or Rocos. - Source: dev.to / almost 4 years ago
  • Machine Learning Best Practices for Public Sector Organizations
    In some cases, such as with edge devices, inferencing needs to occur even when there is limited or no connectivity to the cloud. Mining fields are an example of this type of use case. AWS IoT Greengrass enables ML inference locally using models that are created, trained, and optimized in the cloud using Amazon SageMaker, AWS Deep Learning AMI, or AWS Deep Learning Containers, and deployed on the edge devices. - Source: dev.to / almost 4 years ago
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What are some alternatives?

When comparing Azure IoT Edge and AWS Greengrass, you can also consider the following products

AWS IoT 1-Click - AWS IoT 1-Click is a service that makes it easy for simple devices to trigger AWS Lambda functions that execute a specific action.

AWS IoT - Easily and securely connect devices to the cloud.

Evrythng - Evrythng IoT smart products platform connects consumer products to the Web and manages real-time data in the cloud to drive applications.

Particle.io - Particle is an IoT platform enabling businesses to build, connect and manage their connected solutions.

SAP Edge Services - Learn how key features of SAP Edge Services can help you connect your edge data with the business world and simplify common Internet of Things (IoT) data processing patterns.

Azure IoT Hub - Manage billions of IoT devices with Azure IoT Hub, a cloud platform that lets you easily connect, monitor, provision, and configure IoT devices.

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