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
Google Container Registry
Score 8.0 out of 10
N/A
Google Cloud Container Registry is a place to manage Docker images, perform vulnerability analysis, and decide who can access what with fine-grained access control. Existing CI/CD integrations let users set up fully automated Docker pipelines.
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Pricing
Google Compute Engine
Google Container Registry
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
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Offerings
Pricing Offerings
Google Compute Engine
Google Container Registry
Free Trial
Yes
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
No
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.
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More Pricing Information
Community Pulse
Google Compute Engine
Google Container Registry
Features
Google Compute Engine
Google Container Registry
Infrastructure-as-a-Service (IaaS)
Comparison of Infrastructure-as-a-Service (IaaS) features of Product A and Product B
Google Compute Engine
7.9
66 Ratings
4% below category average
Google Container Registry
-
Ratings
Service-level Agreement (SLA) uptime
8.125 Ratings
00 Ratings
Dynamic scaling
7.861 Ratings
00 Ratings
Elastic load balancing
8.954 Ratings
00 Ratings
Pre-configured templates
9.163 Ratings
00 Ratings
Monitoring tools
3.026 Ratings
00 Ratings
Pre-defined machine images
9.165 Ratings
00 Ratings
Operating system support
8.366 Ratings
00 Ratings
Security controls
8.864 Ratings
00 Ratings
Automation
7.92 Ratings
00 Ratings
Container Management
Comparison of Container Management features of Product A and Product B
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.
As a Cloud Engineer while working on a migration project we used GCR and my experience of using it is actually good the reason behind this is: 1: GCR uses industry-standard encryption to protect your data.2: GCR offers data loss prevention features to help you prevent sensitive data from being leaked or exposed and last but not the least is GCR provides audit logging so you can track who has accessed your data and when, because of these reasons its my go to tool.
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.
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.
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.
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.
It is very easy to integrate GCR with other services and I integrated GCR with GKE and Cloud Build. 1: While working on one project I created one pipeline pulls the app's Docker image from GCR and builds the app's Docker image. Deployed that apps image in GKE. 2: Ive stored the data in GCR and that data was being used by cloud Run applications
As a DevOps Engineer , GCR has made a important contribution to my organisation because GCR can be used to store code and assets, which can help to reduce the development time for new projects.
Using GCR has been a cost-effective solution for us since we only pay for the storage we actually utilise. This has resulted in significant savings on our cloud storage expenses.
We trust GCR because they prioritise the security of our data by utilising industry-standard encryption. This assurance brings us peace of mind, as we know our information is protected.