Google App Engine is Google Cloud's platform-as-a-service offering. It features pay-per-use pricing and support for a broad array of programming languages.
$0.05
Per Hour Per Instance
Microsoft Azure
Score 8.4 out of 10
N/A
Microsoft Azure is a cloud computing platform and infrastructure for building, deploying, and managing applications and services through a global network of Microsoft-managed datacenters.
$29
per month
Watson IoT Platform
Score 8.5 out of 10
N/A
The IBM Watson IoT Platform is an Internet-of-things is a managed cloud-hosted solution supporting device connectivity, control, visualization, and overall device visibility and management. It provides a UI where users can add and manage devices, control access to IoT service, and monitor usage. With its device management service, users can perform device actions like rebooting or updating firmware, receive device diagnostics and metadata, or perform bulk device addition and removal.
N/A
Pricing
Google App Engine
Microsoft Azure
Watson IoT Platform
Editions & Modules
Starting Price
$0.05
Per Hour Per Instance
Max Price
$0.30
Per Hour Per Instance
Developer
$29
per month
Standard
$100
per month
Professional Direct
$1000
per month
Basic
Free
per month
No answers on this topic
Offerings
Pricing Offerings
Google App Engine
Microsoft Azure
Watson IoT Platform
Free Trial
No
Yes
No
Free/Freemium Version
Yes
Yes
No
Premium Consulting/Integration Services
No
No
No
Entry-level Setup Fee
No setup fee
No setup fee
No setup fee
Additional Details
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The free tier lets users have access to a variety of services free for 12 months with limited usage after making an Azure account.
Compared with Microsoft Azure, Google App Engine requires a more complicated development environment setup. It's not as simple as using Visual Studio 2015 with Azure SDK. There are multiple IDE on the market to choose from for developing apps for Google App Engine. JetBrains …
If you have a small team which is also responsible for development of the product then surely go for it. And if you have a larger team with dedicated person to take care of deployments. Go for cheaper options such as compute engine or AWS (be sure to do your research on pricing …
We were on another much smaller cloud provider and decided to make the switch for several reasons - stability, breadth of services, and security. In reviewing options, GCP provided the best mixtures of meeting our needs while also balancing the overall cost of the service as …
I think that Microsoft and Amazon are simply investing more in their offerings, and there are a bunch of cool PaaS solutions out there as well. Google App Engine is solid, and is probably the right choice for some projects. But ultimately one should evaluate each platform …
We have settled with Microsoft Azure considered its effective administration and the ability to data visualization and analysis, together with the top-notch security/stability.
App Engine is such a good resource for our team both internally and externally. You have complete control over your app, how it runs, when it runs, and more while Google handles the back-end, scaling, orchestration, and so on. If you are serving a tool, system, or web page, it's perfect. If you are serving something back-end, like an automation or ETL workflow, you should be a little considerate or careful with how you are structuring that job. For instance, the Standard environment in Google App Engine will present you with a resource limit for your server calls. If your operations are known to take longer than, say, 10 minutes or so, you may be better off moving to the Flexible environment (which may be a little more expensive but certainly a little more powerful and a little less limited) or even moving that workflow to something like Google Compute Engine or another managed service.
Azure is particularly well suited for enterprise environments with existing Microsoft investments, those that require robust compliance features, and organizations that need hybrid cloud capabilities that bridge on-premises and cloud infrastructure. In my opinion, Azure is less appropriate for cost-sensitive startups or small businesses without dedicated cloud expertise and scenarios requiring edge computing use cases with limited connectivity. Azure offers comprehensive solutions for most business needs but can feel like there is a higher learning curve than other cloud-based providers, depending on the product and use case.
For large companies with large-scale manufacturing, electronics management, and infrastructures that require lots of maintenance, IBM Watson IoT is a must-have. It greatly increases performance, decreases downtime, and is an all-around great management dashboard. This is best for infrastructures that have a larger infrastructures of IoT devices in an enterprise/manufacturing setting, otherwise IBM Watson may not bring many benefits to your team.
Microsoft Azure is highly scalable and flexible. You can quickly scale up or down additional resources and computing power.
You have no longer upfront investments for hardware. You only pay for the use of your computing power, storage space, or services.
The uptime that can be achieved and guaranteed is very important for our company. This includes the rapid maintenance for security updates that are mostly carried out by Microsoft.
The wide range of capabilities of services that are possible in Microsoft Azure. You can practically put or create anything in Microsoft Azure.
The asset information is available in both static and dynamic format (from thermal images, to OEM data, to time series data). The ability to ingest all the information using a single platform has great value.
Another benefit is a seamless integration with Maximo. This has been a challenge with other 3rd party systems available.
The experience of IBM Maximo systems updates is positive.
There is a slight learning curve to getting used to code on Google App Engine.
Google Cloud Datastore is Google's NoSQL database in the cloud that your applications can use. NoSQL databases, by design, cannot give handle complex queries on the data. This means that sometimes you need to think carefully about your data structures - so that you can get the results you need in your code.
Setting up billing is a little annoying. It does not seem to save billing information to your account so you can re-use the same information across different Cloud projects. Each project requires you to re-enter all your billing information (if required)
The cost of resources is difficult to determine, technical documentation is frequently out of date, and documentation and mapping capabilities are lacking.
The documentation needs to be improved, and some advanced configuration options require research and experimentation.
Microsoft's licensing scheme is too complex for the average user, and Azure SQL syntax is too different from traditional SQL.
Some of the tutorials could use a little more improvement - While there is a considerable amount of documentation, most of the good documentation for Watson IoT we found to be from third-parties.
Administration and billing tools could use a bit more improvement - The UI is a bit clunky and poorly-designed here.
Be prepared to work with IBM support to get this going - Their turn-around support for inquiries can be lengthy, and setup time may also be quite lengthy because of this.
App Engine is a solid choice for deployments to Google Cloud Platform that do not want to move entirely to a Kubernetes-based container architecture using a different Google product. For rapid prototyping of new applications and fairly straightforward web application deployments, we'll continue to leverage the capabilities that App Engine affords us.
Moving to Azure was and still is an organizational strategy and not simply changing vendors. Our product roadmap revolved around Azure as we are in the business of humanitarian relief and Azure and Microsoft play an important part in quickly and efficiently serving all of the world. Migration and investment in Azure should be considered as an overall strategy of an organization and communicated companywide.
I had to revisit the UI after a year of just setting up and forgetting. The UI got some improvements but the amount of navigation we have to go through to setup a new app has increased but also got easier to setup. Gemini now is integrated and make getting answers faster
As Microsoft Azure is [doing a] really good with PaaS. The need of a market is to have [a] combo of PaaS and IaaS. While AWS is making [an] exceptionally well blend of both of them, Azure needs to work more on DevOps and Automation stuff. Apart from that, I would recommend Azure as a great platform for cloud services as scale.
Good amount of documentation available for Google App Engine and in general there is large developer community around Google App Engine and other products it interacts with. Lastly, Google support is great in general. No issues so far with them.
We were running Windows Server and Active Directory, so [Microsoft] Azure was a seamless transition. We ran into a few, if any support issues, however, the availability of Microsoft Azure's support team was more than willing and able to guide us through the process. They even proposed solutions to issues we had not even thought of!
As I have mentioned before the issue with my Oracle Mismatch Version issues that have put a delay on moving one of my platforms will justify my 7 rating.
We were on another much smaller cloud provider and decided to make the switch for several reasons - stability, breadth of services, and security. In reviewing options, GCP provided the best mixtures of meeting our needs while also balancing the overall cost of the service as compared to the other major players in Azure and AWS.
As I continue to evaluate the "big three" cloud providers for our clients, I make the following distinctions, though this gap continues to close. AWS is more granular, and inherently powerful in the configuration options compared to [Microsoft] Azure. It is a "developer" platform for cloud. However, Azure PowerShell is helping close this gap. Google Cloud is the leading containerization platform, largely thanks to it building kubernetes from the ground up. Azure containerization is getting better at having the same storage/deployment options.
Effective integration to other java based frameworks.
Time to market is very quick. Build, test, deploy and use.
The GAE Whitelist for java is an important resource to know what works and what does not. So use it. It would also be nice for Google to expand on items that are allowed on GAE platform.
For about 2 years we didn't have to do anything with our production VMs, the system ran without a hitch, which meant our engineers could focus on features rather than infrastructure.
DNS management was very easy in Azure, which made it easy to upgrade our cluster with zero downtime.
Azure Web UI was easy to work with and navigate, which meant our senior engineers and DevOps team could work with Azure without formal training.
Positive - This tool greatly increases most company's manufacturing and production efficiency, and the use-cases are extensive. It was a great way of decreasing down-time for us.
Negative - Took lots of work and energy to get up and running, we had to rely on IBM support and tutorials many times during this process.
Positive - Quite cheap, most of the tiers of pricing cost very little, and it's possible to use Watson IoT for a few months for free to see if it is beneficial for your company.
