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
Google Compute Engine
Score 8.6 out of 10
N/A
Google Compute Engine is an infrastructure-as-a-service (IaaS) product from Google Cloud. It provides virtual machines with carbon-neutral infrastructure which run on the same data centers that Google itself uses.
$0
per month GB
SAP Analytics Cloud
Score 8.1 out of 10
N/A
The SAP Analytics Cloud solution brings together analytics and planning with integration to SAP applications and access to heterogenous data sources. As the analytics and planning solution within SAP Business Technology Platform, SAP Analytics Cloud supports trusted insights and integrated planning processes enterprise-wide to help make decisions without doubt.
$36
per month per user
Pricing
Google App Engine
Google Compute Engine
SAP Analytics Cloud
Editions & Modules
Starting Price
$0.05
Per Hour Per Instance
Max Price
$0.30
Per Hour Per Instance
Preemptible Price - Predefined Memory
0.000892 / GB
Hour
Three-year commitment price - Predefined Memory
$0.001907 / GB
Hour
One-year commitment price - Predefined Memory
$0.002669 / GB
Hour
On-demand price - Predefined Memory
$0.004237 / GB
Hour
Preemptible Price - Predefined vCPUs
0.006655 / vCPU
Hour
Three-year commitment price - Predefined vCPUS
$0.014225 / CPU
Hour
One-year commitment price - Predefined vCPUS
$0.019915 / vCPU
Hour
On-demand price - Predefined vCPUS
$0.031611 / vCPU
Hour
SAP Analytics Cloud for Business Intelligence
$36.00
per month per user
SAP Analytics Cloud for Planning
Price upon request
per month per user
Offerings
Pricing Offerings
Google App Engine
Google Compute Engine
SAP Analytics Cloud
Free Trial
No
Yes
Yes
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
—
Prices vary according to region (i.e US central, east, & west time zones). Google Compute Engine also offers a discounted rate for a 1 & 3 year commitment.
A 30-day trial with SAP Analytics Cloud is available, supporting analytics enterprise-wide. A trial can be extended up to 90 days on request.
App Engine is a much more streamlined system than EC2. There is a fundamental difference between them, but they are used for basically the same thing as far a I could tell -- to serve applications EC2 is certainly more complicated, but if offers more machine-level control if …
It's the manageability of the Google App Engine which made it a better option in our case. It's quite straightforward to deploy on App-Engine. No worries for monitoring setup
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 …
You can spawn up your own cluster using Kubernetes or Container Engine which will scale automatically when configured properly, but you have to keep an eye on that cluster. In App Engine you don't have to worry about it at all, just ship your code and it will run.
RunMyProcess is a good solution if you have a relatively straight forward workflow application. However, this solution charges for every page load of the application. If you have a large enterprise customer, these costs can quickly jump if thousands of people are accessing the …
Google App Engine is slower in comparison, and costs more than Google Compute Engine. We chose Google Compute Engine because Google App Engine was way too slow (mainly due to having to use Google Cloud Storage).
The perfect blend of setup flexibility, costing and trust of Google could be my answer to the comparison. This being a server backed service so, ruling out the functions. The Setup flexibility and speed set the GCE apart from Kubernetes. Compliance, regulation and the security …
Google App Engine is a platform as a service where everything is taken care of by Google and we just need to write the code and deploy it onto it. But we hardly have much control over the VMs and the OS offering is not much. We had a limited amount of OS version supported. …
We have used a few other cloud providers of similar services and continue to use Google Compute Engine because it fits well within our technology stack and is cost-effective while providing the service we need. It has allowed us to use and experiment with many more …
We have used Amazon in the past. GCE has come such a long way since then, we have not looked back. IAM and access are on par, cost management is slightly better on GCE. Where we have really seen improvements are the VM types (GCE allows for deep customization that does not …
App Engine is somewhat similar, but we use it together with Compute Engine. App Engine is good for serving end requests to users -- it can scale automatically to any number of requests, but has it's own limitations. Compute Engine does not have any limitations. but you have to …
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.
You can use Google Cloud Compute Engine as an option to configure your Gitlab, GitHub, and Azure DevOps self-hosted runners. This allows full control and management of your runners rather than using the default runners, which you cannot manage. Additionally, they can be used as a workspace, which you can provide to the employees, where they can test their workloads or use them as a local host and then deploy to the actual production-grade instance.
>> Using SAC predictive analytics capabilities for inventory management in a Production line setup has helped generate Purchase Requisitions and Purchase Orders for raw or semi-finished goods without much head-banging into Demand management rules. It does it beautifully with seamless integration with HANA core MM and PP modules, along with BI integration. It has resulted in 30% greater warehouse storage capacity, thereby saving revenue from piled-up inventory and associated manpower costs. >> SAC sometimes shows latency in working out a large data set, thus giving a poor user experience compared to its competition. Also, it may occasionally show misinterpretations when embedding data from 3rd-party systems into the HANA core dataset.
Scaling - whether it's traffic spikes or just steady growth, Google Compute Engine's auto-scaling makes sure we've got the compute power we need without any manual juggling acts
Load balancing - Keeping things smooth with that load balancing across multiple VMs, so our users don't have to deal with slow load times or downtime even when things get crazy busy
Customizability - Mix and match configs for CPU, RAM, storage and whatnot to suit our specific app needs
It makes it easier yo analyse order and related records easily.
We can easily maintain and track the performance of employees in organisation.
Can easily track various aspects for the growth of an organisation thus allowing real time analysis and tracking of organisation's growth and performance.
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)
SAC supports various data sources, but improvements in the ease of connecting to and integrating with certain data repositories, especially non-SAP databases, would enhance the platform's versatility and integration capabilities.
An offline mode for SAC could be valuable for users who need to access and analyze data without an internet connection. Additionally, optimizing performance for large datasets and complex visualizations would contribute to a smoother user experience.
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.
Its pretty good, easy and good performance. Also, interface is very good for starters compared to competitors. Infra as Code (IaC) using Terraform even added easiness for creation, management and deletion of compute Virtual Machines (VM). Overall, very good and very easy cloud based compute platform which simplified infrastructure, very much recommend.
We are planning to review the licensing as we have issues with SAC dealing with huge datasets. Analytics area is good for import models but when we have live connections in place that's when we have issue with SAC dealing with huge datasets in live be it BW or be it HANA models in the backend.
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
Having interacted with several cloud services, GCE stands out to me as more usable than most. The naming and locating of features is a little more intuitive than most I've interacted with, and hinting is also quite helpful. Getting staff up to speed has proven to be overall less painful than others.
On a scale of 1 to 10, I would rate 8 SAP Analytics Cloud's overall usability as a 7. SAC has a clean, modern user interface with drag-and-drop features. It is an integrated platform that combines reporting, planning, and predictive analytics in one tool. It has Real-time connectivity with SAP data sources like S/4HANA.
Self-service analytics capabilities allow non-technical users to build simple dashboards.
Google Compute Engine works well for cloud project with lesser geographical audience. It sometimes gives error while everything is set up perfectly. We also keep on check any updates available because that's one reason of site getting down. Google Compute Engine is ultimately a top solution to build an app and publish it online within a few minutes
I would rate SAP Analytics Cloud an 8 out of 10 for scalability. It offers a flexible, cloud-based architecture that supports expansion across departments and geographies. The platform adapts well to growing data volumes and user needs, making it a strong choice for organizations looking to scale analytics capabilities efficiently.
It works great all the time except for occasional issues, but overall, I am very happy with the performance. It delivers on the promise it makes and as per the SLAs provided. Networking is great with a premium network, and AZs are also widespread across geographies. Overall, it is a great infra item to have, which you can scale as you want.
I would rate SAP Analytics Cloud’s performance an 8 out of 10. Pages generally load quickly, and reports run within a reasonable time frame, even with complex datasets. Integration with other systems is smooth and doesn’t noticeably affect performance. Overall, it’s a responsive and efficient tool for business analytics. But
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.
The documentation needs to be better for intermediate users - There are first steps that one can easily follow, but after that, the documentation is often spotty or not in a form where one can follow the steps and accomplish the task. Also, the documentation and the product often go out of sync, where the commands from the documentation do not work with the current version of the product.
Google support was great and their presence on site was very helpful in dealing with various issues.
Since the implementation stage, the support team has been very helpful and assisting. Even in the later stages, the tech team had quite a rapid response. In general, SAP has provided us with great customer support, let it be for a specific product of SAP or for integration of different modules.
In hindsight, it would have been easier to have someone there in person. Questions were answered, but with 11 participants, it got a bit chaotic online
SAC is a simple solution ad it works fine when connecting it to other SAP tools. On the other hand, connecting it to third party solutions brings difficulties when there's no previous design and the objetives are not clear. It is really important to integrate Business users from the start to provide with valuable business insights
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.
Google Compute Engine provides a one stop solution for all the complex features and the UI is better than Amazon's EC2 and Azure Machine Learning for ease of usability. It's always good to have an eco-system of products from Google as it's one of the most used search engine and IoT services provider, which helps with ease of integration and updates in the future.
SAP Analytics Cloud and Power BI are both tools that help businesses understand their data, but they have some differences. SAC, made by SAP, works well if your company already uses other SAP products. It's in the cloud, easy to use, and has features for analyzing data, getting insights, and planning for the future. Power BI, made by Microsoft, can be used in the cloud or on your own computers. It fits well with Microsoft tools, is easy to use, and can do advanced data analysis. SAC has built-in planning tools, while Power BI needs extra tools for detailed planning
Is good for use across multiple locations. It allows users to access data and reports from anywhere, regardless of their location. Can consolidate data from various sources, including different SAP systems and external sources, which facilitates cross-location analysis. SAC enables access to data and models from SAP Datasphere to create new stories. Detailed permissions can be defined for cross-departmental use.
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.
Many manual data manipulations and exports in Excel have been replaced by the tool, providing management with improved insight into the amount of time spent at each stage of an invoice's lifetime, allowing bottlenecks to be discovered.
We now have more insight into the data, and people with little technical experience can easily build stories.