Close this search box.

Cloud, Edge or Hybrid IoT Solutions: Which Computing Model is Best Suited for your IoT Application?


As IoT systems have evolved to be more mature in terms of scale of operations and diversity in applications. Hence, business organizations have started to rely heavily on three main Data Analysis & Computing models viz: Cloud, Edge, and Hybrid.

Businesses planning to invest in IoT product or system development,  may confront this challenge of evaluating and selecting the most suitable computation model for their IoT project.

This is a critical decision as it is directly related to your IoT Gateway Solution development costs and efforts.

In this blog, we will try to pave the path that can help you to take your first step in the right direction. We will discuss what different computing models are, how they differ from each other, and how the future of each of these solutions looks like.

Understanding the Cloud, Edge and Hybrid Computing Models

  1. Computing at Cloud : A cloud storage based IoT Gateway Solution or Computing at the cloud, refers to a software and hardware networking model wherein the data storage and processing is done at the Cloud Server.A Cloud Storage IoT Gateway also refers to a software and hardware networking device, that provides connectivity and protocol translation services between local application and theCloud based Storage Service.

    Benefits of a Cloud Computing based IoT Gateway Architecture/Model:

    • The advent of cloud computing model made it possible to have devices that are cost-effective with smaller power footprint since the processing and storage operations are managed by the Cloud Back-end in real-time.
    • Since all the device data is stored in a Centralized cloud based back-end, it is easier to manage and apply necessary business intelligence on them.
    • Cloud computing facilitates back-up of critical enterprise data.
    • Businesses can save a lot on capital expenditures due to the flexibility of not owning the on-premise database/computing servers and pay-per-use business model of the Cloud Service Providers.
    Data Computing Models in IoT

    Cloud & Edge Data Computing Models in IoT, Image Source: Medium
  2. Edge Computing: While cloud computing has become very popular amongst the business organizations, but its performance does suffer from the network latency issues.And this has led to the Innovation and Introduction of a more advanced computing model known as the Edge Computing Model!

    Edge computing refers to the processing of data at a point closer to where it is being generated.

    Edge computing, with the help of an IoT gateway device, offers all the advantages of the cloud computing model, at a point closer to the data source and that too  without any latency issues.

    The modern-day Self-Driving Cars can serve as a good example of an Edge Computing use-case.

    It is a no-brainer that “prompt real-time response” is of utmost priority when it comes to the Self-Driving Cars. Any latency in sending valuable data can be prove to be a very costly mistake.  Thus, Self-Driving Cars call for a local data processing solution to make time-critical decisions, as in the edge computing, to mitigate safety hazards.

    Benefits of a Edge Computing based IoT Gateway Model:

    • Edge computing processes the data locally and this enables it to send faster responses back to the data source or end-user application immediately. This also helpings in reducing the system development and operational costs.
    • Edge computing enables the devices in the IoT network to communicate even under offline or low bandwidth conditions. Since the data is stored in the local edge of the network , edge computing makes it easy to deal with unreliable network connectivity environments.
    • Adding an edge node closer to your data sources in the IoT network can help you in filtering and securing the amount or type of data, you would like to be shared with the data center or the cloud.

    You can choose to store the sensitive data locally, while rest of the information can be shared with the third-party Cloud.. Additionally, you can implement security mechanism at the edge to boost the data and network security of the system.

    The following infographic gives an overview of some of the key benefits offered by the edge computing model:

    Edge Computing

    Benefits of edge Computing, Image Source: Altizon
  3. Hybrid Computing: While edge computing scores over the cloud computing in terms of response-time and reliability even in poor network conditions; the ever-evolving business needs still need, in several enterprise use-cases, a centralized hub like cloud to store huge chunks of data.And this business requirement has created scope for the deployment of a Hybrid Computing Model.

    This is the combination of the capabilities of cloud and edge computing: “Processing At The Edge, Storing & Analyzing At The Cloud”

    The hybrid cloud model also enables the business to leverage the best of public and private cloud by integrating them.

    The smart fitness watches, available today can be an ideal example of the hybrid model.

    A part of the application logic or the “smartness” is embedded on the wearable device, while rest of it is implemented in the smartphone app.

    Here, the features of the application and data that should be available to the end-users across devices are stored in the cloud. This enables him/her to access/share the data, or the fitness results in real-time through any device or with anyone, at anytime.

    Benefits of a Hybrid Computing based IoT Gateway Model:

    • Hybrid computing offers a more consistent, secure and faster experience – the data processing happens closer to the data source while it is still stored & managed in a centralized repository by the cloud service providers.
    • You have more flexibility to move data depending on the criticality of key factors including the time, security, priority of data etc.For example, you can keep the data for time-critical decisions closer to your application. On the other hand, you can save large chunks of industrial data, including the historic data, in the cloud repository.

Analyzing the Top Use-Cases for IoT Data Computing

Cloud computing had been a preferred choice for data storage and processing till newer models such as the edge and hybrid models, came into picture.

Even today, many organizations rely on Cloud based IoT Model, when they need a centralized “one-size fits all” solution for data storage & processing. Some of the common examples of cloud computing use-cases include – email servers, storing and retrieving media files on online drives and more

Edge Computing fits in any use-case that demands faster and secure response in near real-time. This means, applications that cannot wait for the data to be sent to the cloud servers, processed and then receive instructions for the next actions. The examples that one can think of could be the

  • Autonomous vehicles ( as discussed already)
  • Oil drilling rigs
  • Disaster Management and Maintenance of Industrial Assets
  • Mining industry

These are the business use-cases that require a localized data processing capability which is closer to the data source; so that any fluctuations in key thresholds can be easily detected and immediate actions can be undertaken.

One of the best use-cases of a Hybrid Computing model can be the predictive maintenance of industrial assets. In this case, the data from millions of machineries can be continuously managed at the edge (IoT gateway device) against certain pre-defined threshold values such as pressure, temperature, vibration parameters.

This data can be stored in the cloud and monitored through admin’s dashboard. Any spike or fluctuation in the threshold value can be alerted to the admin so that he/she can schedule a maintenance activity for the specific device node.

Financial institutions such as banks would rely on the hybrid model so that they can move the time-sensitive data to the edge, while storing the remaining data on the cloud. This will enable them to detect and take prompt actions fraudulent and non-compliant transactions. They can rely on the cloud storage to store crucial data and use this data repository to apply business intelligence to serve their customers better.


IoT Computing ModelsCloudEdge Hybrid
1. Data Processing Point:Data Stored  processed at the cloudData is pre-processed at the edge, closer to the data sourceProcess At The Edge, Store & Analyze In The Cloud
2. Key FeaturesEnables central management of the enterprise  dataReduced response timesOffers a consistent and reliable experience experience throughout the IoT network
3. Business BenefitFacilitates back up of crucial dataEnables data analysis  in near real-timeFlexibility to move data
4. Performance under varying network conditionsNeeds a strong network connectionWorks even in in low-bandwidth locationsCan store data in the edge under poor network condition. Once connection with cloud is established, data can be reliable sent to the cloud.

How is the future of Data Computation in IoT shaping up:

A look at various market reports shows that a majority of the businesses, especially those in the industrial and manufacturing domain, prefer the hybrid computing model.
Let us look at some major market trends related to IoT computation solutions, highlighted by various market researches and surveys:

  • It is estimated that by 2020, 45 percent of all data created by IoT devices will be stored, processed, analyzed and acted upon close to or at the edge side
  • Leading cloud providers such as Google, AWS, Microsoft, C3IoT, Uptake, are eager to establish collaborative partnerships , in the coming times, with edge computing companies.
  • In 2019, Edge computing is expected to dominate the automotive (with regard to Autonomous vehicles) Oil & Gas Industry, healthcare Backed by the convergence of IT and telecom sector, the scale and value of edge computing are expected to rise.
  • Hybrid- and multi-cloud solutions are expected to dominate the most of the industrial IIoT deploymentsMulticloud is the use of multiple cloud computing storage services, a mix of public and private clouds,  in a single heterogeneous architecture. It eliminates the dependency on any single cloud provider.


About the Author

Vaibhav is a digital-marketing professional with a deep-rooted interest in everything automotive. Regular collaborations with automotive tech guys keep him apprised of all new trends in the automotive industry. Besides digital marketing, Vaibhav is fond of writing and music.

Scroll to Top