Security controls (28)
Operating system support (28)
Pre-defined machine images (28)
Monitoring tools (28)
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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
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.
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.
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.
Run, manage, and orchestrate Docker containers directly on Compute Engine VMs or with Google Kubernetes Engine.
Frequently Asked Questions
- 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 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.
- 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.
- 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.
- Project base access.
- Well tested DR scenario.
- Competitive overall prices.
- Less flexible machine type selection.
- Sometimes non-intuitive interface.
- Limited network configuration.
- 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.
- 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.
- 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.
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.
- 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.
- 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.
- Provides resizable compute capacity
- Great scalability and elasticity
- Very customizable
- Generous free tier
- Some loading issues
- Setup can be tricky
- No other problems
- 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.
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.
- 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
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.
Non experienced Google Cloud Compute users will have some extra work when creating a new server. Must have to build it by oneself.
- 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.
- 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
- 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.
- 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.
- Internal applications.
- 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.
- 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.
- 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.
- 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.
- 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).
- 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