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 Cloud Object Storage
Score 7.6 out of 10
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IBM Cloud Object Storage is an IBM Cloud product in the endpoint backup and IaaS categories. It is commonly used for data archiving and backup, for web and mobile applications, and as scalable, persistent storage for analytics.
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Pricing
Google Compute Engine
IBM Cloud Object Storage
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
Lite Plan
Free
Up to 25 GB/mo. For all terms go to https://www.ibm.com/cloud/object-storage
Standard Plan
There is no minimum fee, pay only for what you use
For pricing and all terms go to https://www.ibm.com/cloud/object-storage
Offerings
Pricing Offerings
Google Compute Engine
IBM Cloud Object Storage
Free Trial
Yes
Yes
Free/Freemium Version
Yes
Yes
Premium Consulting/Integration Services
No
Yes
Entry-level 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.
The Lite and Standard service plans for Cloud Object Storage include resiliency options, flexible data classes and built-in security. Pricing is based on the choice of location, storage class and resiliency choice.
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.
In my experience, IBM Cloud Object Storage is well suited for projects like the one I am working on. This includes the use of natural language classification and the uploading of data to train a machine learning model for tag suggestions based on a body of text. Using IBM Cloud Object Storage has helped with this greatly. IBM Cloud Object Storage has also been great for Big Data Analytics thanks to its scalablilty and ease of use for large datasets. Alongside IBM Watson and our team's internal big data tools we've managed to process and analyze data more efficiently, leading to key insights that have driven business value for our clients.
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
IBM Cloud Object Storage is an excellent choice for disaster recovery and backup solutions. Its high durability and geographic redundancy ensure that our backup data is safe and can be quickly restored in case of a disaster. This capability is crucial for maintaining our business continuity and minimizing downtime. We have deployed our loads in an IKS cluster distributed in 3 different AZs with stateful data allocated in COS.
We have a video streaming application and need to store and deliver a vast library of video content to millions of users worldwide, so we store our data in COS, which is cheap and reliable.
We have a bunch of data that must be analyzed and stored in datasets for fraud detection, risk management, and customer insights. In these cases, this data is moved from Onprem to IBM Cloud so we can use cheap storage like COS.
Searching and retrieving—full-text search or metadata search—is one of the significant areas of improvement. It isn't easy to search for data with this.
Integration with other IBM cloud services is trickier. For example, integrating this with API Connect to access the data from API will be difficult for users.
Support - I think you should have more support community.
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.
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.
For my use cases, it has been a very smooth experience. Even my new colleagues have been able to get on top of things very quickly. This shows how easy it is to work with
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.
I have been working in IT sector for more than 15 years. I have worked with various vendors. IBM's sales team, support team have been really helpful. After we start to use their product, their UX design team also contacted us to get feedback from us. They are really interested about our experience.
I just researching and applying the tools on their platforms to ensure a good learning path, based on my needs. Reading the documentation related with resources, tools. Is too big, but I am trying to know more about it every day. It is a good way to know more about their resources. A new way to attract new customers. At the end of the day, we are all involved in improvement and automation of our tasks and resources for customers and end-users.
Yes Our organization used IBM professional services to implement IBM object storage because of its data consistency and multiple way to upload and download data and its encryption security features. Also that its brand matter for the any organization to secure the layer and storage. It sis also verify that application and system are compatibale for this product
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.
Amazon S3 is a great service to safely back up your data where redundancy is guaranteed, and the cost is fair. In the past I have used Amazon S3 for data that we backup and hope we never need to access, but in the case of a catastrophic or even small slip of the finger with the delete command, we know our data and our client's data is safely backed up by Amazon S3. Amazon S3 service is a good option, but based on the features it provides compared with IBM Cloud Object Storage, it is less suitable. IBM Cloud Object Storage is also integrated with more services, like IBM Cloud SQL and IBM Aspera, which AWS does not provide to transfer files at maximum speed in the world.
Scaling up the number of users can lead to significant increases in licensing costs, which, while not a technical limitation, can be a practical constraint for some organizations
This allows us to recommend a platform to our clients that will quickly help them create new, efficient business processes with very little development.
This saves clients hours and days of manual analysis of images, allowing the system to do the work when attaching Object Storage to models.
There is a learning curve in utilizing the storage and the modeling, but once up and running, it works well during deployment.
