Organizations that use cloud services to handle the Internet of Things often employ one of the three largest hyperscale providers — Microsoft, Google or AWS. In this report, we conduct an extensive study, using machine-learning methods, to identify what factors drive the costs of the Internet of Things, and in what scenarios each cloud provider has a cost advantage.
While more than two-thirds of enterprises use edge and near-edge compute assets for IoT analytics, machine and other IoT data eventually finds its way to the cloud - either in its raw form or, more frequently, summarized and transformed for ingestion into cloud-hosted platforms. These platforms differ in how they implement message handling, publish and subscribe bus service, device state
Each of the IoT offerings from the top three providers – AWS IoT Core, Google Cloud IoT Core and Microsoft Azure IoT Hub – is structured differently, in both product features and pricing. In fact, 451 Research’s Digital Economics Unit discovered, for an enterprise to make a cost-comparison of IoT platforms, at least 9 pricing parameters would need to be considered just for a single IoT service. In practice, the complexity arising from performing such comparisons would be impossible without specialized expertise. Each option might be preferred in some scenarios over others, due to specific requirements, where cost is a less decisive factor. However, the services are similar enough that a price comparison is suitable, as they all address a number of key functions.
This Technology & Business Insight report on the Economics of the Internet of Things was compiled through vendor interviews, public websites, and information from 451 Research’s Voice of the Enterprise (VotE) and Cloud Price Index services. Pricing data was obtained directly from cloud providers’ websites and validated using tools and calculators offered by those same cloud providers.
The full report includes:
- Analysis of the Big Three's IoT workload pricing models: On the face of it, some IoT platforms pricing appears simpler than others. But dig down into the depths of the pricing model, and there are subtle nuances that can have a big impact of cost, from the platforms’ definition of “a message” to the rounding up of small data transfers. Here we provide an in-depth analysis of each platform’s model.
- Worked examples of different pricing scenarios: With nuances having such an impact on cost, here we provide a specific example, which aims to help enterprises understand how their costs are calculated for different workloads.
- Decision tree: We provide specific recommendations in the form of a decision tree, which reveals which factors most impact total costs, and which provider is cheapest in which scenario.
- Conclusions & recommendations: Finally, we provide guidance for enterprises looking to consume IoT services, and for service providers and vendors wanting to take advantage of the new opportunities for IoT.