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.
Easier Way to Create Client Apps
Google Compute Engine Review
Google Compute Engine || Alternate to AWS EC2
Highly Available and Cost Effective Cloud Provider
GCE - Reliable and cost effective cloud infrastructure provider
Google Compute Engine is Amazing!
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 …
Awards
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Popular Features
- Security controls (44)7.070%
- Operating system support (44)6.969%
- Pre-defined machine images (43)5.656%
- Pre-configured templates (42)5.252%
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
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
- 8.2Dynamic scaling(41) Ratings
Ease of scaling up or down in response to customer needs
- 7.7Elastic load balancing(37) Ratings
Automatic balancing and distribution of resources across multiple virtual computers
- 5.2Pre-configured templates(42) Ratings
Pre-defined templates for virtual machines
- 3Monitoring tools(27) Ratings
Monitoring tools provide alerts when problems are detected
- 5.6Pre-defined machine images(43) Ratings
Range of different server configurations available
- 6.9Operating system support(44) Ratings
Range of operating systems available as pre-configured images
- 7Security controls(44) 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?
Virtual machines for any workload
Online VMs on high-performance, reliable cloud infrastructure offered on preset or custom machine types for web servers, databases, or AI.
Includes one e2-micro VM instance, up to 30 GB storage, and up to 1 GB of outbound data transfers free per month.
Preset and custom configurations
Prebuilt samples called Jump Start Solutions can be used to deploy an application in minutes, such as a dynamic website, load-balanced VM, Java application, three-tier web app, or ecommerce web app.
Offers predefined machine types, sizes, and configurations for any workload, from large enterprise applications, to modern workloads (like containers) or AI/ML projects that require GPUs and TPUs.
For more flexibility, a custom machine type between 1 and 96 vCPUs with up to 8.0 GB of memory per core can be created. Also offers many block storage options, from flexible Persistent Disk to high performance and low-latency Local SSD.
Industry-leading reliability
Compute Engine boasts strong single instance compute availability SLA: 99.95% availability for memory-optimized VMs and 99.9% for all other VM families. Offers live migration to maintain workload continuity during planned and unplanned events. When a VM goes down, Compute Engine performs a live migration to another host in the same zone.
Automations and recommendations for resource efficiency
VMs can be added automatically to handle peak load and replace underperforming instances with managed instance groups.
Resources can be manually adjusted using historical data with rightsizing recommendations, or capacity for planned demand spikes can be guaranteed with future reservations. All of Google's latest compute instances (C3, A3, H3) run on Titanium, a system of purpose-built microcontrollers and tiered scale-out offloads to improve infrastructure performance, life cycle management, and security.
Pricing and discounting
Google offers detailed pricing guidance for any VM type or configuration, and a pricing calculator to get a personalized estimate.
To save on batch jobs and fault-tolerant workloads, Spot VMs are offered to reduce costs. Automatic discounts for sustained use are offered, or up to 70% off when signing up for committed use discounts.
Security controls and configurations
Encrypts data-in-use and while it’s being processed with Confidential VMs.
Defends against rootkits and bootkits with Shielded VMs.
Meets compliance standards for data residency, sovereignty, access, and encryption with Assured Workloads.
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 Screenshots
Google Compute Engine Videos
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
(171)Attribute Ratings
Reviews
(1-25 of 44)A Robust and Scalable Cloud Infrastructure Solution. Improve Performance and Cost-Effectiveness of Our Applications.
- Scalability. Allows us to scale our resources up or down based on demand.
- Cost efficiency. Offers a pay-as-you-go pricing model.
- Flexibility. Provides a wide range of VM configurations and customization options.
- Top-notch security features. Helps to protect our data and applications.
- Unparalleled customer support.
- Although the cost is effective, for some small business enterprise this might be a big issue because the cost can be a bit higher.
- Implementation and configuration may take time especially for the non-technical users.
Easier Way to Create Client Apps
- Multiple Web Apps Options
- Prebuilt Samples
- Customization
- Documentation For First Time Users
- More Implementation Would be Handy
Google Compute Engine Review
- Runs various operating systems. It also does the patch management of those OS
- Virtual Manager allows management of OS
- Provides high performance block storage
- Provides global load balancing
- Subnetworks are not supported
- Support is quite expensive
- The user interface can be complex for a first time user
Google Compute Engine || Alternate to AWS EC2
Google Compute Engine provides a high level of customization, flexibility, & scalability in contrast to this, storage is also a major role that it plays. Performance and Security are the Real Factors to go for the GCP Compute Engine
- Web and Application Hosting
- High Performance Computing
- Security, One can rely on Security related things if we are preferring Compute Engine.
- Ease of Use
- Pricing
- Configuring Network & Setting up of VPC and all can be Improved.
Highly Available and Cost Effective Cloud Provider
- Scalability and Flexibility - during peak hours or sudden spikes in traffic to our website, GCE automatically provisions additional virtual machine instances to handle the increased load.
- Global Network Infrastructure - we can deploy multi-region architectures with ease, distributing workloads across multiple regions for improved redundancy and performance.
- Advanced Security Features - encryption at rest and in transit, identity and access management (IAM) controls, network firewalls, and distributed denial-of-service (DDoS) protection
- Costing - more visibility over how costing is calculated
- More pre-configured templates like AWS has cloudformation
- Advanced Monitoring Tools are needed
Unsuited - low traffic websites, static websites, legacy applications, small scale web apps
- Easy to scale
- Transparant costs
- Wide range of services
- Feature parity with AWS
- Improved security tools
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
Great for Auto Scaling
- UI
- Integrations
- Billing
- Their UI is a bit clunky
- Overcrowding on hosts
- Locations
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.
Compute Engine supports all of our needs
- It can run containers.
- It is completely flexible in terms of CPU, memory, etc.
- It's APIs are useful for spinning up machines computationally.
- Running multiple containers on one VM.
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.