Overview
What is Google Compute Engine?
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.
Impact of Google Compute Engine on the Organisational Infrastructure
High productivity and good ROI with unique services
Google Compute Engine: Powering Business Growth and Efficiency!
Google Cloud Engine - A powerful IAAS solution
- Building products
- Running integration test
- Running …
How Google Compute Engine helped us save money
Google Cloud - your dev friendly alternative to AWS and Azure
There are pros and cons...
Google Compute Engine a strong Compute Engine platform from Google Cloud Platform
S/4 Hana appliance for the internal training and learning purpose in …
Great for Auto Scaling
Perfect for rapid developments and scalability
Google Compute Engine is great and cost effective for simplified use cases
GCE: A reasonable choice of deploying scalable applications
Compute Engine supports all of our needs
Google Compute Engine. It's good.
Awards
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Popular Features
- Operating system support (38)7.272%
- Security controls (38)6.868%
- Pre-defined machine images (37)5.757%
- Pre-configured templates (36)5.050%
Reviewer Pros & Cons
Pricing
Preemptible Price - Predefined Memory
0.000892 / GB
Three-year commitment price - Predefined Memory
$0.001907 / GB
One-year commitment price - Predefined Memory
$0.002669 / GB
Entry-level set up fee?
- No setup fee
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Starting price (does not include set up fee)
- $0 GB
Product Demos
Google Compute Engine Load Balancing, a quick introduction
Computing with Google Compute Engine
RouterOS CHR deployment in Google Compute Engine (GCE) demo
Creating Custom Images for Google Compute Engine
Hands on with Load Balancing on Google Compute Engine
Features
Infrastructure-as-a-Service (IaaS)
IaaS provides the basic building blocks for an IT infrastructure like servers, storage, and networking, in an on-demand model over the Internet
- 8.1Service-level Agreement (SLA) uptime(26) Ratings
The service uptime as a percentage defined in the SLA
- 8.4Dynamic scaling(35) Ratings
Ease of scaling up or down in response to customer needs
- 8Elastic load balancing(31) Ratings
Automatic balancing and distribution of resources across multiple virtual computers
- 5Pre-configured templates(36) Ratings
Pre-defined templates for virtual machines
- 3Monitoring tools(27) Ratings
Monitoring tools provide alerts when problems are detected
- 5.7Pre-defined machine images(37) Ratings
Range of different server configurations available
- 7.2Operating system support(38) Ratings
Range of operating systems available as pre-configured images
- 6.8Security controls(38) Ratings
Compliance with security protocols like SSL and AES
- 7.9Automation(2) Ratings
Automation of administrative tasks
Product Details
- About
- Competitors
- Tech Details
- FAQs
What is Google Compute Engine?
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.
Predefined Machine Types
Virtual machine configurations for micro instances to instances with up to 160 vCPUs and 3.75 TB of memory
Custom Machine Types
Or create and customized, virtual machines with the shape (i.e. vCPU and memory) needed for each project
Persistent Disks
Network storage, up to 64 TB in size, can be attached to VMs as persistent disks. Create persistent disks in HDD or SSD formats. If a VM instance is terminated, its persistent disk retains data and can be attached to another instance. Take snapshots create new persistent disks from that snapshot.
Local SSD
Always-encrypted local solid-state drive (SSD) block storage. Local SSDs are physically attached to the server hosting the virtual machine instance. Local SSD sizes up to 3 TB are available for any VM with at least 1 vCPU.
Global Load Balancing
Distributes incoming requests across pools of instances across multiple regions.
Batch Processing
For running large compute and batch jobs using Preemptible VMs. Features fixed pricing and no contracts or reservations. Activated by checking a box when creating the VM, and then they can be turned off when the work is done.
Containers
For running, managing, and orchestrating Docker containers directly on Compute Engine VMs or with Google Kubernetes Engine.
Google Compute Engine Features
Infrastructure-as-a-Service (IaaS) Features
- Supported: Dynamic scaling
- Supported: Elastic load balancing
- Supported: Pre-configured templates
- Supported: Pre-defined machine images
- Supported: Operating system support
- Supported: Security controls
Google Compute Engine Competitors
Google Compute Engine Technical Details
Deployment Types | Software as a Service (SaaS), Cloud, or Web-Based |
---|---|
Operating Systems | Unspecified |
Mobile Application | No |
Frequently Asked Questions
Comparisons
Compare with
Reviews and Ratings
(169)Attribute Ratings
Reviews
(1-25 of 34)Google Compute Engine is Amazing!
Currently, we face no problems with this service and everything is working more than fine.
- Running services and applications
- Secure access
- Easy replication and backups
- It is totally easy to use
- Would be nice if prices drop a little bit
With Google Compute Engine instances, you don't need to worry much about machine maintenance and upgrades, and instance monitoring is easy and clear since Google provides cool dashboards to check machine status and notify you about required updates or changes.
It helped us in identifying the key lacking areas/bugs during the initials phases of a product development.
- Networking via VPC (Virtual Private Cloud)
- Big Data Analysis/IoT
- Storage/Databases for Data Transfer
- Machine Learning for cloud based translations
- Third party integrations are very tricky, UI can be improved
- Artificial Intelligence is an area that can be clubbed together with Machine Learning for better result and future customisation
- Networking tools can be simplified for better structuring
Machine Learning is a tool which is more efficient than any other provider and has wide range of languages for processing.
High productivity and good ROI with unique services
- 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
- The pricing model can get a bit convoluted at times
- While the integration with other Google Cloud services is pretty slick, linking up Google Compute Engine with services outside of Google's ecosystem isn't always smooth sailing
- The learning curve for more advanced Google Compute Engine features can be pretty steep at times
- Cost-effective
- Scaling
- Good-looking UI Dashboard
- Documentation
- Networking Configuration
Especially for those use cases where incoming traffic might change a lot in a time period.
Is it less appropriate instead if no high traffic is expected.
Google Cloud Engine - A powerful IAAS solution
- Building products
- Running integration test
- Running accreditations tests
Before every release we set up a production grade deployment using Google Compute Engine and do performance test.
- Running jenkins jobs
- Running performance tests
- Simulate a production environment before each release
- GCE is only available only in three regions, They can add more regions.
- Google can provide pricing model like AWS's reserved instances and spot instances .
- They can improve the UI a bit better, when I get started I found it hard to get familier with the UI.
How Google Compute Engine helped us save money
- Better interface than AWS
- Service has been very reliable
- Excellent security policy structure
- Documentation could be improved
- Sometimes is hard to estimate how much we are going to spend
- Fast
- Great CLI
- Great APIs
- gcloud CLI is very broad
- Billing detail could get more finer grained
There are pros and cons...
- Fast return of files.
- Cost effective.
- Good UI.
- The document security has been an issue.
- Not a lot of customization ability.
- Customer service people weren't very knowledgeable.
S/4 Hana appliance for the internal training and learning purpose in Google cloud platform environment.
- VM Instances
- Storage
- Bare metal solution
- Quota limit for Compute Engine API Persistent Disk SSD
- Quota limit for Compute Engine API A2 CPU
- Quota limit for Compute Engine API N2 CPU
Perfect for rapid developments and scalability
- Uptime
- Automated backups
- Strong security posture
- Feature parity with other cloud providers
- Total cost transparency
- Free training
- Auto Scaling
- Flexible Instance Sizes
- Easy to understand pricing model
- More inside the UI advanced capabilities would be nice
- Customer is currently forced to learn the CLI to do advanced functions / scripting
- Stability is just not the same as other cloud providers in our experience.
- It's very easy to spin up a VM from the console. We can say the console is very user-friendly.
- Set of predefined VMs ready to be used for different needs.
- Options of preemptible VMs which help reduce the considerable amount of cost.
- Spin up takes some time and that's where containers come into the picture.
- Setting up security for created VMs is still messy and it can be simpler.
Google Compute Engine. It's good.
- It is easy to use
- It is easy to setup
- Configuration and monitoring of the instances is straightforward and thorough
- The configuration of ingress and egress for the nodes could be easier
- Machine image storing, compression, etc. could be better or have more functionality
- Transferring machine images to and from my local environment is something I have wanted on GCE and its competitors for a long time
Now, to address the question of recommending GCE to a colleague, ultimately the organization will have to make a decision regarding the entire cloud platform. It wouldn't make much sense, outside of a special case, to use GCE for some parts of your cloud infrastructure and a competitor on other parts.
That practical caveat aside, I believe that the GCP brings a strong suite of tools to the table overall and is good value for money at this time as well.
Developer familiarity to certain competing platforms can be a sticking point, but a colleague who is already asking for a recommendation is likely already open minded about moving to GCP.
Why GCE can be considered as a DR multicloud scenario
- Project base access.
- Well tested DR scenario.
- Competitive overall prices.
- Less flexible machine type selection.
- Sometimes non-intuitive interface.
- Limited network configuration.
Great Service
- GCE is well suited for multi-environment testing, development, and experimentation.
- Very cost effective.
- If you want to do something outside of a standard image it can be a little cumbersome.
Best in breed mid market cloud compute: GCE
- East interface to scale up and down the compute capacity
- Easy, straight forward billing and chargeback capabilities
- Reliability / uptime is great and had no issues so far on uptime
- Works well in multi cloud environments
- Although not always used, there is room for adding more detailed and granular management console when things go wrong (and sometime they do)
- Documentation can sometime be hard to find especially for using GCE for time critical, large scale deployments
- There are also some compatibility issues when running custom libraries over GCE. Support for third party drivers and libraries can be improved.
The Foundation for Cloud Processing
- Internal applications.
- Tools.
- QA and testing environments.
- Production deployment.
- Experimentation with new technologies.
- Migration from datacenter to cloud environments.
- Training environments (they can be easily created and then deleted after the training).
- Business processing.
- Data processing and pipelines (in combination with various other products available in Google Compute Platform (GCP).
- Easy and fast creation of the resource.
- Rich ecosystem of tools and cloud technologies.
- Ability to scale up and down, based on the needs.
- Better documentation.
- Up to date documentation.
- Capabilities on par with AWS.
- All situations where one needs to allocate compute capability. Google Compute Engine offers a variety of server configuration and one should be able to find a matching configuration, except for largest servers or mainframes. This still may be the case for large relational databases in enterprises.
- Processing confidential information if the organization does not master security in cloud environments. One cannot simply transplant an application from a private data center to the cloud and expect the same security. Security needs to be designed and implemented from the start.
- Period workloads processing events. For that, consider Serverless/Function as a Service which is also a offering on Google Compute Platform.
Google Cloud Engine -- a cost-benefit comparison is sorely needed. Because everything else is there.
- Compute Engine is gaining traction, and documentation is getting easier to find.
- Menus and services are structured more intelligently.
- The idea of poor Support from the Google brand prevents my technicians from picking up the phone.
- It's easier to find EC2 experts to consult and support mission-critical operations.
Excelent cloud solution
- Access files and data with higher security.
- Easy to manage.
- Create virtual machines very easily.
- Google Compute Engine is user-friendly.
- The price is good.
- Documentation can be more detailed.
- More costs options.
- No other recommendations.
Ease to start, easy to maintain
GCE is in play primarily for our engineering department as well as our customer engineering and sales teams.
- GCE is excellent at cost management. We are able to manage billing to the second and set up rules to manage those expenses easily.
- GCE is fast! Our teams constantly provision/de-provision workloads and GCE is able to keep up well, no matter the type or number of servers that need to be spun up.
- The configuration is extremely easy. The UI is being improved and tweaked on a regular basis to keep up with UI/UX trends and make it easy for users to do everything from the console. That said, the API is extensive and powerful. Many of us prefer the CLI for bulk actions.
- Windows management is lacking. When managing a Windows machine, it's nearly always necessary to RDP into the machine and an agent would be very helpful for system-level API calls.
- Stackdriver integration could be rolled out better. We would like to see more standard monitoring functionality and metrics built-in for instant deployment when using a new project.
- Inter-project organization. It's difficult to connect different GCP projects in order to share a VPC. Once that is complete, it's nearly impossible to extricate them.
The best cloud solution
- Provides resizable compute capacity
- Great scalability and elasticity
- Very customizable
- Generous free tier
- Some loading issues
- Setup can be tricky
- No other problems
Reliable and user-friendly IaaS platform
- A friendly and intuitive graphical interface is available
- There are several resources available, such as networking and snapshots.
- The performance is amazing and you can select the region/zone close to your region.
- There is a shell environment that helps a lot
- Better price for Windows Server virtual machines
- The graphical interface to manage a specific VM could be improved.
Google delivers
- We are able to select a custom amount of vCPU and Memory resources.
- It provides pricing estimates on the page when configuring a new instance (versus having to reference separate documentation).
- We are able to tie into G-Suite User Directory for access control to the Google Compute Engine console.
- It would be nice to move a Google Compute Engine Server to a different project without having to recreate it.
Host scalable and globally distrubuted compute systems to get best value for money in cloud
- Advanced autoscaling logic to cater scenarios that involve high load at the global level.
- Seamless and reliable rolling updates with support for releases.
- Backup data via very fast snapshots helps to quickly back up systems
- Good support for things like metadata (pre-defined and custom).
- Ability use Windows client OS VMs (or support import capability)
- Increase the offered default monitoring metrics set.
- Adjustable shutdown cooldown period (instead of fixed 30 seconds window).
- Great scalability. The cloud VMs all have elastic specs functionality, but re-scaling some VMs may create a significant amount of downtime for your backend.
- GPU offerings. Google Cloud offers NVIDIA Tesla K80s, P4s, and P100s, which some of the cloud computing competitors don't offer.
- Downtime, Google's SLA is very good. I've never had a poor experience with downtime or maintenance on their services.
- Internet speed can be quite variable. The bandwidth for different instances ranges a lot. Some instances have had internet bandwidth that is in the range of 5-10x the speed of other instances.
- Customizability. Customizing the number of cores, RAM beyond what Google offers in their standard compute plans can get quite expensive.
- Firewalls/networking. Figuring out how to use these took way longer than necessary. Getting the right ports opened and forwarded took lots of reading, something that other services included in the creation/initialization process of virtual machines.