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
IBM Power Virtual Server
Score 9.2 out of 10
N/A
IBM presents their Power Systems Virtual Server as a scalable, cost-effective way to run IBM AIX, IBM i and Linux workloads.
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Pricing
Google Compute Engine
IBM Power Virtual Server
Editions & Modules
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
No answers on this topic
Offerings
Pricing Offerings
Google Compute Engine
IBM Power Virtual Server
Free Trial
Yes
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
Yes
Entry-level Setup Fee
No setup fee
Optional
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.
IBM Power System components are more reliable and have longer uptimes than those of competing systems. The IBM Power System Virtual Server setup and configuration process was simple. To now, no errors have been made in terms of placing an unreasonable burden on the server, …
One of the greatest alternatives to IBM Power System Virtual Server is Microsoft Azure. First, I'd want to point out that it's a lot more expensive than the competition. Because it is less expensive than other virtual servers, even small and medium-sized businesses may afford …
IBM is very competitive and has most of the same features as the other products. The other products did not have a cost-competitive hybrid cloud model and primarily were cloud-based.
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.
It is really impactful in terms of scenarios like ERP systems and Data Analytics where heavy data needs to be analysed in terms of volume and their needs to be high scalability offering so in that scenario it is a great asset and features like distribution of workload using AI capabilities by leveraging modern IBM offerings like Watson is really helpful the area in which it could improve is native development of application in terms of adoption of New cloud Technologies
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
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.
At the moment we are 100% satisfied with the performance and our support team is well used to the process involved. So unless we have some major issues in adopting, we are sure to be with IBM itself.
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.
I would rate IBM Power Virtual Server’s overall usability as an 8 out of 10. The platform offers a solid interface and intuitive dashboard, making it relatively easy for users with cloud experience to navigate. Its scalability and flexibility are strong points. However, the learning curve for new users can be steep, especially when dealing with complex integrations or configurations. While documentation and support are extensive, some users may find the setup process challenging. Overall, it’s highly functional but could be streamlined further for beginners.
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
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.
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.
As with most IBM products the ongoing support for IBM Power Virtual Server is solid and consistent. IBM provides a clear roadmap for receiving support of their products. Both voice and online response is offered. It is obvious that IBM has the internal systems and culture to maintain support functions. This starts from the initial support call to the problem analysis and continues through the problem resolution. Documentation and communication are consistent within this process.
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.
They both have their own ups and downs and it totally depends on the team which suits them best. IBM Power Virtual Server has Performance, Scalability, Reliability and Availability, Compatibility, and Good Vendor Support. The specific use case and workload requirements played a significant role. Some workloads may benefit from IBM Power Systems' architecture, while others may perform equally well on alternative platforms.
I would rate IBM Cognos Analytics’ scalability as a 9 out of 10. The platform is highly capable of handling large volumes of data and supporting thousands of users with ease. Its architecture is designed for high performance, though it may require fine-tuning for extremely complex data environments to maintain optimal performance.
There have also been 80% fewer application crashes due to a lack of resources that previously ran on the X86 platform.
Administration management has been simplified and staff can dedicate themselves to the development of applications, instead of providing support to users when the applications do not respond efficiently, this made staff 45% more productive.
