Google Compute Engine Reviews

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Score 8.9 out of 101

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Reviews (1-24 of 24)

Aditya Mohan profile photo
Score 9 out of 10
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Google Compute Engine (GCE) is used for most of the AI workload spanning both on-prem private cloud and public cloud. It is used for both onetime training phase for our Deep Learning workload as well as ongoing Deep Learning inference for customer facing applications.
  • 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.
GCE is very well suited to small to mid scale deployments. It is also run very well with AI workloads especially when using Google TenserFlow et al. It is less appropriate for extremest scale deployments that spam multiple data center (probably because of lack of document on best practices)
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Brendon Brown profile photo
Score 7 out of 10
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We use Google Compute Engine for staging deployments in the web development department. We are operating off an external assessment that competitor services offer better pricing on production units, so we've agreed to keep Compute Engine in a low tier staging system only. We have yet to do our own audit from an internal efficiency perspective, and how that will impact pricing assessments.
  • 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.
In my opinion, most cloud applications should consider Google Compute Engine from a speed and pricing perspective. Of course, you should do an assessment based on what your application needs to do and how it needs to perform, but there's a machine for most kinds of deployments. If you have the expertise nearby, all the better.
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Thomas Young profile photo
Score 9 out of 10
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Google Cloud Compute Engine is used by the organization to do automated advanced analytics for clients across the world. The software addresses two important aspects of our business: first, it runs in the background and with speed and accuracy that works well for the analyst as well as others; second, the software runs in the cloud, which makes analyzing the data generally easier.
  • Google Cloud Compute Engine does a good job at crunching numbers.
  • Google Cloud Compute Engine is great at always being available, and I have yet to find any latency.
  • Google Cloud Compute Engine is great for doing advanced analytics (machine learning) without needing the software on my desktop.
  • Google Compute Engine is in the cloud, which means that it is probably less secure than on-premise options. With that said, I have never had a problem.
  • Google Compute Engine seems fine at running machine learning models, but is in no way as good as competing tools that are not run in the cloud.
  • Google Compute Engine is less user friendly than AWS or Azure, at least that's my experience.
Google Cloud Compute Engine is well suited for companies doing advanced analytics, including machine learning and artificial intelligence. The software is less useful for smaller companies with only small data sets available for analysis. The software works well if you need instant access from anywhere in the world and if you are used to the Google infrastructure.
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Tristan Dobbs profile photo
Score 10 out of 10
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Reseller
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We use compute engine across a number of departments at our company. We deploy temporary workloads to VMs on a daily basis and have deployed our production systems to GCE for deployment and CI/CD pipelines, ETL for data projects, and for large-scale customer engineering.
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.
GCE is so customizable and extensible that it can fit most use cases and applications. Costs are excellent and it has become the most useful tool in our collection to deploy products, test scenarios, migrate workloads, and move out of the "server room". Ease of management cost control and customization are the biggest wins with GCE.
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Vinicius Lima profile photo
Score 10 out of 10
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We use Google Compute Engine as IaaS provider across the whole organization. Almost all of our servers are deployed in this environment. We also help some of our customers to deploy virtual machines due to our expertise in cloud computing. We think it is a reliable platform that delivers everything we need regarding IaaS.
  • 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.
As any IaaS provider, Google Compute engine is suitable for any kind of companies, from small to large ones. You can easily create new servers and start configuring it in minutes. Usually, the prices are lower when comparing with buying a local server. Furthermore, Google Compute Engine is integrated with other services from Google Cloud platform.
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Stephen Groat profile photo
Score 8 out of 10
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Google Compute Engine is being used for Google Cloud Platform centralized workloads. With AWS as a primary cloud, GCE and GCP are used to support customers centralized in those systems. It is used by the entire organization, primarily to support customers in those environments with minimal latency. Since G Suite is also deployed, the tight integration is a benefit.
  • Strong G Suite integration.
  • Strong Infrastructure as Code (IaC) features.
  • Easy to configure.
  • Less availability than AWS.
  • Can hide critical configuration features necessary for detailed performance tuning.
  • Features can remain in beta for a long time.
Google Compute Engine (GCE) and Google Cloud Platform (GCP) offer strong contenders in the cloud market, especially with the strong G Suite integration. For companies who already have G Suite that are looking to start using a cloud platform, adding a small number of cloud resources with GCE and GCP can be an easy entrance into the cloud market.
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Josh Ashby profile photo
Score 10 out of 10
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Verified User
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The big advantage of using Google Computer Engine is that pricing is much less than if you bought your own hardware. You are only paying for what you need, there's no over-spend required for redundant systems. Google Compute Engine is one of the best IaaS cloud solution at the moment.

  • Provides resizable compute capacity
  • Great scalability and elasticity
  • Very customizable
  • Generous free tier
  • Some loading issues
  • Setup can be tricky
  • No other problems
You can use Google’s web services to build a highly customized solution to meet all your company’s needs. Very easy to build, test, deploy and manage applications and services though their data centers. Google Compute Engine gives you the freedom to move away from having your servers based on premises which can reduce costs.
Read Josh Ashby's full review
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October 28, 2019

Great Service

Score 9 out of 10
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We use Google Compute Engine to host a variety of services including proxy servers, small websites, and peripheral services. Additionally, we make use of the Google Compute Engine to quickly spin up instances for testing and experimentation and then destroy them once finished without incurring a lot of costs, both money and time.
  • 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.
Google Compute Engine is particularly well suited for multi-environment testing, development, and experimentation with the low cost of spinning up small instances along with the speed at which it can be done. It also does well hosting small websites and proxy servers that don't see a lot of network traffic.
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Score 9 out of 10
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Google Compute Engine is used for a variety of workloads. Its use is growing as larger parts of the organization start embracing cloud computing.
  • 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.
Google Compute Engine is well suited for:
  • 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.
Google Compute Engine is less well suited for:
  • 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.
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Score 10 out of 10
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Verified User
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Using Google Compute Engine, you can manage your costs. It helps to avoid the high costs of buying hardware and setting up a new server infrastructure on your physical location. Also, Google Computer Engine offers you the possibility of accessing your files and data with higher security, and save days worth of time spent on setup and installation. We migrated from hardware to visualized servers hosted to Google Compute Engine. We made Windows and Linux servers and hostel our core-services: ERP, accounting, marketing app.
  • 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.
Google Compute Engine offers you the possibility to scale your computing resources up or down quickly. Is a flexible solution that can be utilized by everyone who wants to manage software without having to worry about hardware. It's easy to create servers, and can connect to servers by RDP or SSH. It's a great choice for small and big organizations. We migrated all our critical servers from hardware to visualized servers and we reduced our cost significantly. It is more efficient to migrate you ERP to virtualization server to Google Compute Engine that on your hardware.
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June 28, 2019

Google delivers

Score 9 out of 10
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Google Compute Engine is used by the DevOps team for hosted Windows servers. This alleviates countless concerns with having a server on-site, and is very comparable to other hosting providers.
  • 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.
Google Compute Engine is very similar to AWS EC2, and in most of the same regions, but also in a couple of additional geographic locations. So if proximity is crucial, there are some additional options with GCE over AWS EC2.
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Score 8 out of 10
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We use Compute Engine for one of our main consumer and business products. We have a globally distributed infrastructure for this. I believe we are using almost all the features/functionalities offered by GCE. e.g Instance, groups/templates with full rolling updates capability coupled with CI/CD. We also host part of our monitoring systems in GCE. Other than that, some other teams are on the POC phase for some critical backend products as well. Moreover, we experiment with some of our machine learning on Compute Engine. The benefit we get by using GCE is the combination of the autoscaling, rolling updates, preemptability, and global network.
  • 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).
Suited: Compute Engine is really good for any monolith type services which you need to host a service globally. And it works very well. Not Suited: autoscaled services that have long/heavy post-processing activity during termination.
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Score 10 out of 10
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We use Google Compute Engine to support almost all of our in house computer. We have a web application that has a distributed backend of various workloads, all of which run on Compute Engine. Some of these workloads are even containerized. These workloads support the whole organization and provide analysis for research and development.
  • 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 is suitable for any scenario that fits one of the pros I listed. It is very flexible and easily configurable. In many cases, VMs need to be spun up on the fly programmatically, and without orchestration software, one can accomplish this by using one of the APIs.
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Fedor Paretsky profile photo
Score 7 out of 10
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We use Google Compute Engine for creating cloud virtual machines that are GPU-accelerated. Because we have centralized most of our backend services to DigitalOcean, we use Google Compute Engine for most of the services that DigitalOcean doesn't offer. Additionally, we use the custom image functionality that GCE offers to create VMs with custom images.
  • 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.
For companies that require GPU-accelerated instances, GCE may be your only good option. They offer a lot of services that aren't available at the next best cloud computing platform (in my opinion), DigitalOcean. Beyond the functionality, the UI and documentation can be approved a lot, but if you're used to the way Google designs their developer tools and APIs, then you're probably all set with moving forward with Google's Compute Engine.
Read Fedor Paretsky's full review
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August 17, 2018

Google does it right

Score 10 out of 10
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We use it for running managing docker containers inside clusters. GCE provides the flexibility for auto scaling and managing application performance easily. It used for running all the applications across departments
  • Custom machine types gives us the flexibility of defining the right cpu and memory
  • Load balancers are efficient
  • Easily create instances using gCloud SDK
  • Very little programming languages support
  • Charged before usage
  • Changing platforms is not easy
Small businesses can easily scale their applications with little up front cost in setting up the infrastructure for their application.
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Tyler Johnson profile photo
Score 9 out of 10
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Verified User
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We use Google Compute Engine to host our cloud-based web application. We manage a multi-node, shared instance of our application for thousands of monthly users, as well as individual, dedicated instances for a few of our larger clients. We are a fairly small organization and GCE manages all of our hosting needs.

GCE is very straightforward to use, most of our engineers interact with it on a daily basis. Using GCE means that we can forget about the pains of maintaining computing hardware and just focus on making great software. As a Google Apps user, we also benefit from GCE's rich integration with the rest of the Google product line. Picking GCE over competitors was an easy choice for us.
  • A simple web-based interface that is a breeze to train new engineers to use. Our experienced engineers never have trouble finding or doing anything on GCE.
  • Sustained use and Committed use discounts mean we get top-tier VMs for an incredibly competitive price.
  • Wonderful identity and access management that gives us peace-of-mind when granting access to machines to contractors and other 3rd parties.
  • Fast VMs, lastest in hardware, and enough RAM to power even the hungriest of our services.
  • Built-in monitoring via Stackdriver is quite expensive for what it provides.
  • Initially provided quotas (ie. max compute units one can use) are very low and it took several requests to get an appropriate amount.
  • Support on GCE is limited to their knowledge base and forums. For more hands-on support provided by Google, you must pay for their Premium services.
We are a web software company and GCE has been great for hosting our web applications. We have a single-node and multi-node instances and GCE never misses a beat. We also have some Windows Server clients, so launching and testing our software in Windows is made possible by GCE. When comes to reliably hosting web infrastructure at scale, GCE is a fantastic choice.
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Dmitry Sadovnychyi profile photo
Score 10 out of 10
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Compute Engine is a general purpose VM on top of which you can run basically any software you want, including Windows. They are priced on per-hour basis with discounts when you use it for a long time, and also with an option to run a VM for a short time with significant discount, but at a risk that it could be terminated at any time (Preemptible VM) – it's very useful for data processing since you can spawn tens of VMs for cheap and even if they are terminated you can just do it again later.
  • Per-hour pricing with sustained use discounts -- you'll get a good discount if you run a VM for a long time.
  • Always free usage limits -- you can run a small VM on it completely free of charge!
  • Preemptible VM – huge discounts when you only want to run it for a short time, but it could be terminated if there's a demand.
  • GPU support -- useful if you want to control your ML training jobs by yourself instead of using their Cloud ML APIs.
  • Sustained use discounts could be combined with committed use discounts -- just give me cheaper price if I'm running a VM for a year
Compute Engine provides VMs which you have to maintain by yourself, but you can run anything you want in there. Consider using App Engine for web sites, Container Engine for anything that needs to be often deployed and scaled automatically (if it doesn't fit into App Engine). On Compute Engine you can easily spawn up an instance with hundred GB of memory, do your stuff, and shut it down after couple of hours. You will only pay for what you used. But be careful -- there's some people who "forget" about running instances and then they are surprised with huge bills in the end of the month -- this is your responsibility to turn them off, since Google will do their part to make sure they are working flawlessly without any downtime.
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Sazzad Hossain Sharkar profile photo
Score 9 out of 10
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Google Compute Engine is for maintaining our organizational department wise projects such as online training applications, online student registrations, blog posts, tutorials, etc.

We need to separate our organization projects in the various department because these applications are developed for solving several problems for filtered users. Google Compute Engine is used for hosting our online students' maintenance and other related tasks.
  • Google Compute Engine gives us easy ability to maintain the servers including live statistics about what is going on.
  • Easy single click to extend server system including network changing. Easy to clone servers between multiple regions.
  • Google Compute Engine has one-click installer (pre-built) applications including Bitnami launchpad.
  • Easy to integrate with cloud storage and backup periodically source codes and database to storage facility
  • Sometimes it is hard to remember the settings menus because these are separated into various sections.
  • Inviting external users to projects or maintainers are also a little complex
  • Server images have limited facilities. I mean some of the sections are disabled and had to be re-enabled on my own. Especially for Debian or Ubuntu images.
  • SMTP service is disabled by system and needs to be configured Postfix on my own.
  • UI is little complicated.
If anyone, especially developers, whom are a newbie, they can quickly launch pre-built servers with desired applications easily. The projects come with the git repository that can rapidly be installed with a few single clicks.

If you have good experience, you can make a bridge to back up the project source codes or files to cloud storage quickly by doing some little functioning. There are manuals online for working with GCE

Non experienced Google Cloud Compute users will have some extra work when creating a new server. Must have to build it by oneself.
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Raymond Hawkins profile photo
Score 9 out of 10
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We're a small custom software company with one main web-based program which we host on Google Compute Engine. Over the years we've gained more and more users and we were having issues scaling up as we grew, sometimes slowly and sometimes rapidly. Finally our old hosting solution had a bad down period and we were forced to look elsewhere to get our software back online. Since switching to Compute Engine, we've been able to save money while scaling up as needed. It has been very reliable and overall a perfect fit for us.
  • Compute instances can be resized with quite minimal down time.
  • They offer recommendations when an instance might need to be upgraded to improve performance or downgraded to save you money.
  • The ability to SSH into any of your instances from any browser or mobile device works extremely well and is very useful!
  • The cost of bandwidth is somewhat high.
Google Compute Engine is really great for web-based software that needs high availability and flexibility in scaling. I wouldn't necessarily recommend it for simple website hosting under most circumstances as the bandwidth can get expensive quickly if serving large files, but even for that it's very reliable and easy to set up and get started.
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Andy Zhang profile photo
Score 8 out of 10
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It is being used across the whole organization. It addresses the problem of providing solutions to customers who need an all-inclusive infrastructure solution for web services and apps. It has a lot of systems that are managed by Google which people commonly build themselves without knowing that they are reinventing the wheel.
  • Clean and well-designed API
  • Simple, yet transparent reporting of usage
  • Generous and straightforward pricing
  • Missing GPUs for cloud instances
  • Excessively lean customer service department
  • Confusion as to how the container environment works in relation to GCE
I would recommend Google Compute Engine for companies with adept engineering teams that want to maximize the value of their talent and focus on the core strengths of their business (product, R&D, research, UX), rather than waste resources on infrastructure. The engineering team should have good knowledge of the tools available and how to stitch them together into a scalable product.
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David Long, SPA profile photo
Score 10 out of 10
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We develop software for our clients and lean on Google Compute Engine and Google Container Engine for hosting those applications. These applications are used both across our clients' organizations as well as publicly by customers of these clients. We made the decision to use Google Compute Engine in order to reduce costs while getting solid reliability from a VPS platform. Google has provided us with both of those needs.
  • Spinning up new systems is a breeze. We are able to auto-scale our container engine clusters easily based on CPU usage or resource reservations.
  • Cost is ~1/2 of AWS in general. Google advertises this and so far they've been true to their word. They provide sustained-use discounts if you run systems that stay online for an entire month.
  • The command line interface is very easy to use. Setting up new environments is simple since the process can be scripted through the command line.
  • The L7 load balancer can be difficult to get set up. It's limited in its functionality, especially with the container engine.
  • It's hard to find certain objects on the web console. Often times the things I need to get to are buried in advanced menus.
  • Google's decision to only support MySQL on their relational DB service means that I have to manage Postgres instances in Compute on my own, managing everything from storage to backups.
If running a Kubernetes or any container engine environment, Google Compute is simply the best. Given that Kubernetes and containers in general are still fairly new in terms of widespread usage, there are hangups, but those seem to exist in any hosting platform. Google's terminology, as compared to Azure and AWS is also really easy to understand. If you want logging, it's called logging. If you want storage it's called storage. Where Google Compute falls short is the same as where all cloud providers fall short: if you want high resource systems that are always online, it will get expensive really quickly.
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Score 10 out of 10
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Google Compute Engine (GCE) is Google's answer to AWS. We use GCE as a low-cost virtual host solution, and we purposefully migrated off AWS and Rackspace to GCE. We made the decision mostly based on cost, but also the flexibility of the toolkit and deployment. GCE is a one-stop shop for hosting virtual and scalable environments in the cloud.
  • GCE is an excellent tool for quick deployment of on demand servers.
  • GCE offers the ability to snapshot servers, create host clusters, and auto-scale based on demand.
  • GCE is the most cost effective virtual hosting environment we've used. They give up front pricing which is a major plus for us.
  • I can't think of any immediate areas GCE could improve on.
GCE is well suited for building scalable tools in the cloud. If you have an on demand service, GCE is a great tool for spinning up and down servers quickly. GCE can get expensive for full time applications using high network throughput. If you have a lot of data moving between servers, make sure you understand the cost associated with how information will be flowing over the network.
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Score 8 out of 10
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We use Google Compute Engine to support public facing clinical research patient recruitment services, the service is scalable to accommodate increase in capacity due to news items that sign post patients to the services. The platform is IaaS and data is shared with major health charities.
  • Google Compute Engine provides rapid scalability without the need to worry about the infrastructure itself, this means we can focus on core development of the service.
  • The platform makes it easy to link in to other Google apps and APIs.
  • Google does not lend itself to legacy technologies where you may require a cloud migration strategy.
  • While Google tends to solve their own problems and share solutions it can feel in bit one-way.
Google Compute Engine is great for rapid deployments and for green field developments however other cloud providers are geared to providing a migration path and strategy to support legacy services. Where analytics are concerned Google wins as they have a mature solution. Google tends to keep things simple and the environment encourages you to make use of the latest technology especially in the web services area.
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Score 9 out of 10
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It is being used by a department. It is used to host our school department's website, which is used by both teaching assistants and students.
  • UNIX-style command line tools
  • Web-based console panel
  • Persistent disks
  • Web console is a bit slow
Since it is still relatively new compared to Amazon's EC2, Google Compute Engine is not as appropriate for projects that need to be as reliable as possible. It is, however, fine for small projects.
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Feature Scorecard Summary

Service-level Agreement (SLA) uptime (23)
9.5
Dynamic scaling (21)
9.4
Elastic load balancing (18)
9.5
Pre-configured templates (23)
8.8
Monitoring tools (24)
8.2
Pre-defined machine images (24)
8.8
Operating system support (24)
8.3
Security controls (24)
8.9

About 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
Distribute incoming requests across pools of instances across multiple regions.

Batch Processing
Cost effectively run large compute and batch jobs using Preemptible VMs. Fixed pricing and no contracts or reservations make it easy: simply check a box when you create the VM and turn them off when the work is done.

Containers
Run, manage, and orchestrate Docker containers directly on Compute Engine VMs or with Google Kubernetes Engine.

Google Compute Engine Competitors

Amazon Web Services, Microsoft Azure, Amazon S3 (Simple Storage Service)

Google Compute Engine Technical Details

Deployment Types:SaaS
Operating Systems: Unspecified
Mobile Application:No