Methodology of IoT platform selection by mobile virtual network operators
Costs And Challenges Of IoT Platforms
Describing some of the common challenges and costs associated with many IoT platforms.
In the tech world, buzzwords quickly become the norm. Such is the case, for instance, with IoT (the Internet of Things,) which is now becoming more and more invited with open arms by companies big and small.
But it would be unwise to merely look at the benefits of IoT without considering the challenges these companies may have to face when choosing the right IoT platform to put their trust in. And for many businesses, the main challenge is the price tag because IoT doesn't come cheap.
But there may be a solution to this predicament, one which may cut costs and directly address specific challenges with IoT adoption. But first thing's first.
Below are the top four IoT platforms and the upfront costs companies can look forward to:
1. IBM Watson IoT Platform
IBM's own IoT is made to help businesses get their IoT operation started. It's a fully managed cloud-hosted service created to adopt the technology, streamlining storage, device registration, and data visualization.
IBM's pricing plans include:
Blockchain service: $200/month
Analytics services: $300/month
Connection service: $500/month
All of IBM's pricing plans include technical support.
Amazon's IoT solution bases its pricing on four components: connectivity, messaging, device shadow usages, and rules engine, and are billed separately for each. AWS IoT core doesn't have pricing plans like IBM but offers a calculator, which companies can use to estimate their monthly cost.
Note that the rates may vary depending on the area the company is located in.
3. Google Cloud IoT Core
Google charges based on the volume of data used in a month:
Up to 250 MB: $0
250 MB - 250 GB: $0.0045 / MB
250 GB - 5 TB: $0.0020 / MB
5 TB and above: $0.00045 / MB
For all cases, Google allows unlimited registered devices within QPS maximums and imposes a minimum charge of 1024 bytes.
4. Microsoft (Azure IoT)
Azure IoT's pricing is based on the total number of messages a day, with a message being defined at 4 KB. Therefore, if a particular message goes over, it will be counted as two messages or more, depending on the size of the message.
The platform offers two pricing plans, Basic and Standard, each with three edition types.
B1: $12.80 / month (400,000 messages per day)
B2: $64 / month (6 million messages per day)
B3: $640 / month (300 million messages per day)
Standard plan (the number of messages per day is identical to the Basic plan editions):
S1: $30 / month
S2: $320 / month
S3: $3,200 / month
The Standard plans offer additional cloud-to-device messaging, device management services, and access to IoT Edge.
Additional Costs Companies Should Consider
The actual costs of deploying, managing, and scaling these solutions in IoT projects won't be found on any of these companies' pricing pages. However, the platform computing and infrastructure cost is only a fraction of what companies have to invest in rolling their IoT projects adequately.
To these costs, one needs to add:
And in some cases, there may even be a learning curb to consider
Such costs don't always appear prominent, but companies that want to hop onto the IoT Operation front must be accounted for in the bottom line. The problem is that the final amount may prove to be too much for companies to bear, just by looking at these issues and keeping in mind the different types of potential pricing plans an IoT platform presents.
And this can happen both for smaller businesses with fewer resources to spare and bigger enterprises that may be working with more and more data as they move forward. But, creative IoT adoption just may be the solution.
An Unexpected Solution: AI integration
IoT adoption can be greatly simplified through the use of Artificial intelligence and IoT automation. All IoT-centered services ultimately follow the same logical pattern in which data is created, communicated, gathered in the same place and stored, and then is analyzed before a specific action is taken.
The ability to enroll in the final step, the action, ultimately depends on how well the information is analyzed, which is where Artificial Intelligence can play a crucial role. IoT provides the data, and AI learns what to do with it, which can streamline a lot of different areas in IoT adoption:
Allowing fast and accurate analyses
Managing and obtaining insights from the data
Allowing better customizations, especially when it comes to data privacy
Artificial Intelligence-managed IoT platforms (AIOps) greatly help reduce labor costs and prepare businesses and enterprises for fully operating in the future. In addition, AIOps can also be integrated with edge computing solutions.
Edge computing significantly improves the way companies and enterprises collect and analyze their data, as it processes information near the source, not in the cloud. As a result, edge computing allows companies to make data-driven decisions.
Some analysts predict that by 2024, the global edge computing market will rise to USD 9 billion, though only 56% of professionals have tangible plans on how to decentralize their computing.
But given that AIOps and edge computing can directly solve most of the challenges associated with IoT adoption, it may be a better strategy to cut out the middleman and focus this form of a creative technological solution.
Telivity Allows Easy IoT Adoption
Televity is a modern IoT solution that aims to help businesses make better decisions through the use of data.
Through Telivity, companies and enterprises can improve their IoT automation process. The platform leverages years of experience and analytics to automate and enhance IoT, using unique solutions to enable connections to digital systems and devices.
Contact us at email@example.com to learn more about Telivity and request a free consultation.