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Google Kubernetes Engine OverviewUNIXBusinessApplication

Google Kubernetes Engine is #6 ranked solution in Container Management software. PeerSpot users give Google Kubernetes Engine an average rating of 7.8 out of 10. Google Kubernetes Engine is most commonly compared to VMware Tanzu Mission Control: Google Kubernetes Engine vs VMware Tanzu Mission Control. Google Kubernetes Engine is popular among the large enterprise segment, accounting for 64% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a comms service provider, accounting for 16% of all views.
Google Kubernetes Engine Buyer's Guide

Download the Google Kubernetes Engine Buyer's Guide including reviews and more. Updated: September 2022

What is Google Kubernetes Engine?

Kubernetes Engine is a managed, production-ready environment for deploying containerized applications. It brings our latest innovations in developer productivity, resource efficiency, automated operations, and open source flexibility to accelerate your time to market.

Google Kubernetes Engine was previously known as GKE.

Google Kubernetes Engine Customers

Philips Lighting, Alpha Vertex, GroupBy, BQ

Google Kubernetes Engine Video

Google Kubernetes Engine Pricing Advice

What users are saying about Google Kubernetes Engine pricing:
  • "It is competitive, and it is not expensive. It is almost competitive with AWS and the rest of the cloud solutions. We are spending around 3K USD per month. There are four projects that are currently running, and each one is incurring a cost of around 3K USD."
  • "I would rate Kubernetes' pricing four out of five."
  • "The price for Google Kubernetes Engine could be lower - I'd rate its pricing at three out of five."
  • Google Kubernetes Engine Reviews

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    Patryk Golabek - PeerSpot reviewer
    CTO at Translucent Computing Inc
    Real User
    Top 5Leaderboard
    Extremely scalable, easy to setup, and has good machine learning
    Pros and Cons
    • "The deployment of the cluster is very easy."
    • "Our critique is that we have to do too much work to get the cluster production-ready."

    What is our primary use case?

    We have everything in Kubernetes. We're basically moving everything from the cloud into Kubernetes - inverting the cloud. We have all that built for the CIT pipeline and have our tools within the cluster.

    This is to support application development. The application side is always within the cluster. We have a security cluster. So everything is there. We have a database within the cluster as well. We don't need a managed database. There's a cloud database due to the fact that we use Kubernetes database. Everything goes into the cluster.

    It makes it easy for us to be consistent across different environments, including development environments or in Oracle environments, as everything runs within the cluster.

    What is most valuable?

    The solution allows you to work on and from multiple clouds. You can use Google's cloud, or mix and match clouds across suppliers.

    You can split into regions within your own cloud.

    The deployment of the cluster is very easy. You just click a button and it's deployed, or just run a simple command and it deploys itself. You don't have to go through the steps of installing the cluster yourself. It's already deployed and managed.

    The master of the cluster is also managed by Google. If there are any updates, they are responsible to handle that. It just takes a little bit of a load from our task load. You don't have to manage the master, or the version of the cluster yourself. 

    You don't have to think about the installation process. They take care of the underlying infrastructure deployment and managing the versioning of the cluster. When we need to update, it's simple. They'll help us to easily, smoothly update those cluster nodes. You don't have to deal with that either.

    When it comes to the Google Cloud, the Kubernetes advantage that's there for machine learning is that they have a CPU, which is a central processing unit, which is much faster than GPU. If the clients are willing to pay for it, we'll run the machine learning jobs within the Kubernetes cluster, then connect to Google CPU, which gives us the ability to finish the job much, much faster.

    What needs improvement?

    It's maybe a controversial topic, as Kubernetes itself should be just your bottom layer. However, within your own engine, you expect to do more with time. Since we're putting so much into the cluster, it would be nice if some of this stuff was already done, baked into the cluster.

    Our critique is that we have to do too much work to get the cluster production-ready. Most people just start it and think that's production. That's not really production. That's just bootstrapping the cluster, with all the tools that you need.

    A lot of people rely on cloud tools, or a cloud-built system, to get going. We would like to have that baked into the cluster. Due to our usage pattern of the cluster and how heavily we use it, our expectation is to have more tools baked into the cluster. There should be more emphasis on tools developed immediately from the cluster to support application development versus relying on third-party vendors, like Jenkins. 

    The third-party vendors have to adapt to Kubernetes, and that creates a problem, as there's always a delay. Third parties don't have much incentive to do anything right away. That means we have to wait for these guys to catch up. We don't have a big enough team to actually change every open source code, as there's so much of it.

    For how long have I used the solution?

    We started using Kubernetes in 2015, around the time it started. Whenever Google launched their tools is about the time we started. Before that, we used Kubernetes, however, we were deploying it ourselves.

    Buyer's Guide
    Google Kubernetes Engine
    September 2022
    Learn what your peers think about Google Kubernetes Engine. Get advice and tips from experienced pros sharing their opinions. Updated: September 2022.
    633,184 professionals have used our research since 2012.

    What do I think about the stability of the solution?

    The solution is stable.

    The solution is cloud-native and every cloud is using basically the same version. That's what makes it easy for us to move between clouds. Google wants users to integrate with their own cloud storage and security, however, which is where issues can arise. 

    It allows you to create private clusters. There's no competitive advantage for cluster clouds right now, which is good for us, as it's a uniform-looking ecosystem that allows us to move between clouds easily.

    What do I think about the scalability of the solution?

    Kubernetes is designed to scale horizontally and vertically as well. It scales quite well.

    We have unlimited scaling through horizontal scaling. We can add more Kubernetes nodes. When applications do grow, we need to maybe horizontally scale applications or our databases. We just kick off another node when we need however much memory CPU and just keep on scaling it. Obviously, you pay for it, however, scaling is extremely easy.

    A lot of time we automate the scaling as well. Based a little bit on AI and cloud automation processing, we detect the CPU usage or the GPU usage and if we exceed a certain threshold, the cluster automatically adds another data node, so it's self-serving. So scaling is almost automated around cases that we do.

    The system knows by itself how to scale dynamically. The dynamic elastic scaling is baked into our systems as well. When people do use, for example, the machine learning special cluster, that one goes automatically from zero to 23 nodes depending on the users. A lot of times, we do shut it down, with just no usage. When people kick off a job, it automatically spins up on a new cluster node and deploys the job, gets another job, and spins up another one. It dynamically grows. We have to allocate pools that we have of an increased cluster in Google Cloud and it works well.

    How are customer service and support?

    We basically are able to solve our own problems on our end. We don't need the assistance of technical support. We might have used it once in the past six or seven years and that is it.

    How was the initial setup?

    The initial setup is very straightforward. Everything is basically done for you. It's nice and easy.

    We can do the cluster management. There are cluster administrators who take a more serious role. They are responsible for the disks backing some of these applications, the databases, deployment of these tools, the infrastructure tools, et cetera.

    What about the implementation team?

    We have application developers which work with the administrators to actually deploy the application, as you still have to just be meaningful in the cluster, and add some sort of business logic. Those have to work with our administrators so we get that done and we do support, and a lot of different tools, monitoring tools for visibility. It's important to us, because if we do, we follow a microservice architecture across applications. They're like little black boxes and we have to be able to see inside of them.

    What's my experience with pricing, setup cost, and licensing?

    Multi-cloud is a sort of an expensive endeavor as the tools are overpriced. We're looking at options that aren't based on Anthos, which is Google's multi-cloud solution. 

    While you pay money to Google, they also take a piece of the action as well.

    CPU is very cheap, however, GPU is very expensive. If you want to iterate on your data client's tasks within a Kubernetes cluster, it will cost you.

    There is no licensing cost. You pay for the cloud and you pay for what you use based on the CPU and RAM usage based on the VM, the virtual machines. The cluster is still made up of computers, so you pay for the computers that are backing the clusters. If you kick off a Kubernetes node, which has three nodes in your cluster, you have to pay for each of these nodes, these computers, these virtual machines that you get bootstrapped with. You just use the machine time as with any cloud and you get a price in Google for the machine type and your machine type is defined based on your CPU and RAM usage. If you want to have 60 GBs of RAM, you pay for that RAM or for CPUs.

    The same thing is true if you ask for a GPU computer, as most of the virtual machines don't come with a video card unless you ask for it. Then you have to pay for that the computer and the video card

    What other advice do I have?

    We're actually certified as well Kubernetes vendor.

    We're using version 1.19. The most up-to-date is 1.20. We're never on the latest version, we're always like a version behind, or even two versions behind, to give them time to sort through their issues. We're using 1.19 in both Azure's, Google Cloud's, and EKS, however, EKS might be two versions behind, maybe.

    Most of the time we're deploying in, as a private cluster within the cloud. It's isolated from public infrastructure. That's for security reasons. We don't want our cluster to be exposed to the public internet.

    We also have a hybrid deployment Azure on-premises. This is just to make things easier for integration purposes. On-premise, it's connected to the cloud and then we can just use the same tools to be Kube-Native source. We develop the same tools for Kubernetes and then we can just deploy Kubernetes on-premises or in the cloud, it doesn't matter.

    We also are doing multi-cloud as well, and we're deploying from Google Cloud into AWS.

    With Azure, we have one giant cloud right now. That way, we can partition a cluster and see multiple clouds and multiple visions. If Google Cloud goes down for whatever reason, as it happened, two years ago, due to bad configurations, too many clusters in a cloud, we're covered. We do multi-cloud as the solution is critical and we can't afford to have it go down.

    We are basically are a full-service company. We do everything for our clients - including application development and everything that entails.

    I'd advise users to take security seriously. Don't just deploy things on the internet. Make sure your cluster's secure. You want to be able to tell your clients that you have a secure implementation of a cluster. That requires a little bit of cloud set up with every cloud to create a private network, private subnetwork, manage the ingress and egress, so input and outputs of the requests coming into your cluster.

    These are things you have to think about when you deploy, just initially before you get started. All the clouds support it, you just have to know how to set up your VPC, virtual private network connection tier with every cloud and how to set up subnets to isolate your cluster to specific subnets, so it's not exposed on the internet, it's private and then any requests coming from the internet have to go to your load balancer directly to your cluster.

    However, if you manage that and some peoples' requests going out of your cluster, you won't be able to manage those as well, since they're on NAT and a cloud router as well. So you know what's coming in, what's going out. You can monitor your traffic coming in and within your cluster.

    These days a lot of people just use the containers directly from third-party sources or public repositories in the docker containers in which the Kubernetes cluster runs and those could come with malware. You want basically, in the main cluster, to have security policies implemented for every cluster. You don't get that from your cluster loggers. You have to get that from third-party vendors. This is where the competition comes with the Kubernetes. 

    In general, I would rate the solution at an eight out of ten.

    Which deployment model are you using for this solution?

    Public Cloud

    If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

    Google
    Disclosure: I am a real user, and this review is based on my own experience and opinions.
    PeerSpot user
    Patryk Golabek - PeerSpot reviewer
    CTO at Translucent Computing Inc
    Real User
    Top 5Leaderboard
    Secure solution for our data pipelines that allows us to run fintech and healthcare applications
    Pros and Cons
    • "We can scale it all the way from a single zone to multiple regions around the world."
    • "One of the biggest issues right now is the Kubernetes backup system. That's being handled right now by Google, but it's in beta."

    What is our primary use case?

    We use Kubernetes for our data pipelines. For everything else, we use the standard version of GKE, and we manage the whole stack. It's a bit higher than infrastructures as a service. It's more of a platform as a service. Kubernetes is the platform we use to build our staff products.

    We're a Kubernetes certified company. Everything in the cloud, we push toward Kubernetes. You can move Kubernetes between systems, and we're deploying the system into Kubernetes securely. We're running a lot of fintech applications and healthcare applications, so both financial data and patient healthcare is essential. That's one of the reasons we moved to Google Cloud, because the implementation is a little more secure.

    Kubernetes is the number one tool used by every company in the last five years because it's a dynamic container runtime engine, and we ship software in containers.

    We've been shipping software containers since 2014. We switched to Kubernetes around 2016 or 2017. We switched completely to Kubernetes as our container runtime engine. That's how we ship software and maintain managed software. Kubernetes is the primary tool for all our work that we do in DevOps.

    What needs improvement?

    There are multiple flavors of GKE. That's why we deploy it for different use cases. There are different issues with each of the use cases. When it comes to using Kubernetes as a commodity, which means allowing Google to manage your virtual machines, they still don't have all the features baked in. Our issue is that you have no ability to change it because Google manages it. Our biggest issue right now is that it just requires a little bit more control over some of these Google managed versions of virtual machines.

    One of the biggest issues right now is the Kubernetes backup system. That's being handled right now by Google, but it's in beta. There are no fundamental issues like we have in EKS or Azure with a private cluster. They nickel and dime you with features or they're pushing you to their own observability tools. We want to use our own observability tools.

    The whole thing is integrated with Google monitoring and logging all that stuff in the cloud, so it's not necessarily bad. It's just that we want to use our tools. Service mesh management is an issue as well. There's only one service management, so we would like to use console service mesh so we can use the managed project there. We don't like the way Google deploys things. We like to deploy things ourselves, and that can cause friction in terms of how we deploy things. We have to spend a little bit of extra time on coding. Fundamentally, I don't see any issues with it right now.

    What do I think about the stability of the solution?

    The stability is good.

    What do I think about the scalability of the solution?

    The scalability is good.

    In terms of deploying multi-regional clusters or multi-zonal clusters, and multi-cloud, we can do that. Most of the time for development, we use a single zone just to save money, but for our staging environment, we use multi-zonal in a single region. Then when we go to production, we use multi-regional. We can scale it all the way from a single zone to multiple regions around the world. 

    Now with Anthos, which is the multi-cloud version of Kubernetes and Google Cloud, it allows us to deploy Google Cloud Kubernetes into AWS or Azure. That means that we don't have to use EKS; we can just deploy Kubernetes and Amazon through Google Cloud, so we have one portal to manage everything.

    How are customer service and support?

    We haven't needed to use technical support yet.

    Which solution did I use previously and why did I switch?

    We use Amazon EKS for some clients. When we write software, a lot of times we try to keep the deployment in-house. But if it's just the client, they have their own deployment DevOps team.

    How was the initial setup?

    It's straightforward. We did everything in Terraform. All of our infrastructure is codified. We basically codify our whole infrastructure. We have a deployment platform to plan this and to be able to deploy all the tools in Google Cloud. It's easy for us to manage. If there are any issues, we can always change that in our code, and manage it through just code. The accessibility is nice.

    Auditing those cloud resources is very easy for us. It gets quite expensive as well. If you don't keep your tabs on it from a business point of view, you're always going to check the billing to make sure that there are no services that aren't being used efficiently. Google is fully committed to Terraform scripts. 

    I think every part of the communication has been updated from just basic CLI to Terraform scripts, which is nice. The Terraform, Google Cloud module is almost fully baked. It's basically a mature tool as well. It makes things a little bit easier for us to manage.

    What other advice do I have?

    I would rate this solution 8 out of 10.

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
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    Buyer's Guide
    Google Kubernetes Engine
    September 2022
    Learn what your peers think about Google Kubernetes Engine. Get advice and tips from experienced pros sharing their opinions. Updated: September 2022.
    633,184 professionals have used our research since 2012.
    Girish Kumar Dandamudi - PeerSpot reviewer
    Solutions Architect at a tech services company with 11-50 employees
    Real User
    Competitive pricing and easy to set up, use, and scale
    Pros and Cons
    • "The feature that I like the most is the ease of use as compared to AWS. Its ease of use is very high, and I can quickly deploy clusters with a simple template."
    • "Their documentation is a little here and there. Sometimes, the information is not clear or updated. Their documentation should be a little bit better."

    What is our primary use case?

    We have somewhere around 20 microservices that we need to deploy for our product. We are using Kubernetes Engine to deploy those 20 microservices.

    What is most valuable?

    The feature that I like the most is the ease of use as compared to AWS. Its ease of use is very high, and I can quickly deploy clusters with a simple template.

    What needs improvement?

    Their documentation is a little here and there. Sometimes, the information is not clear or updated. Their documentation should be a little bit better.

    They have a good marketplace, but it is still evolving and is not as mature for some services.

    For how long have I used the solution?

    I've been using this solution for about a year.

    What do I think about the stability of the solution?

    It is pretty stable. I did not encounter any problems.

    What do I think about the scalability of the solution?

    In the last 10 months, scaling was easy. There is an auto-scaling feature where I can just provide them with how many nodes I require, or I can create custom node pools where, for particular applications, I can deploy certain nodes. I haven't had any issues with scalability.

    We have four or five users. They are DevOps engineers. Only two users are the owners. They have deployed Kubernetes Engine, and they manipulate the workload. Its usage would be twice or thrice a week.

    How are customer service and support?

    I haven't encountered any issues so far, and I haven't raised any doubt with them. So, I haven't interacted with them. We are currently managing four or five projects with Kubernetes Engine. We haven't had any issues with any of them.

    How was the initial setup?

    It is easy to set up. In AWS and other clouds, the support for Kubernetes is minimal, but in Google Cloud, it is much easier to set up. I would rate it a five out of five in terms of ease of setup.

    What about the implementation team?

    It was set up in-house.

    What's my experience with pricing, setup cost, and licensing?

    It is competitive, and it is not expensive. It is almost competitive with AWS and the rest of the cloud solutions.

    We are spending around 3K USD per month. There are four projects that are currently running, and each one is incurring a cost of around 3K USD.

    For Kubernetes, the components have to be broken down into machine cost, storage cost, and application cost, if you are using any private applications or any images. If you look at the compute instances for GCP, there are hundred different tiers. For example, you have general-purpose E2 machines, then you have high-compute machines, and then you have the GPU machines for T4. Those are pretty standard compute instances. Kubernetes Engine is just a layer on top of these compute instances to control your entire microservices architecture.

    I would rate it a four out of five in terms of pricing.

    What other advice do I have?

    I am yet to explore all of its features. I would rate it an eight out of ten.

    Which deployment model are you using for this solution?

    Public Cloud

    If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?

    Google
    Disclosure: I am a real user, and this review is based on my own experience and opinions.
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    Navin Gayar - PeerSpot reviewer
    Associate Architect at Wipro
    Real User
    Top 20
    A scalable solution with great autoscaling features
    Pros and Cons
    • "The solution is available across AWS, GCP and Azure and is seamless."
    • "The console for this solution could be improved because it is very limited."

    What is most valuable?

    The autoscaling feature is the most important feature in Google Kubernetes Engine. It also supports containerization. The solution is available across AWS, GCP and Azure and is seamless.

    It does not require much investment if you move from AWS to GCP or Azure. The entire cloud process remains the same, with only minor changes.

    What needs improvement?

    The console for this solution could be improved because it is very limited. In addition, features like a desktop for the Docker image, drag and drop into a node could be added.

    In terms of visualization, the solution could also provide some integration with other tools. We have heard about Grafana and AppDynamics offering these features, but they still require better tools. Google is going in the right direction because they believe in open source integration and give opportunities to other players to work with them. However, they should provide packages where they integrate more features to demonstrate more encouragement in the marketplace.

    Training, tutorials, tooling, samples, and more case studies should be included in the next release.

    For how long have I used the solution?

    We used this solution for one year.

    What do I think about the stability of the solution?

    The solution is stable, and I think even more stability will occur over time. Google Kubernetes Engine is moving in the right direction, especially when you consider concepts of Docker image, pods, nodes, clusters, control planes, data planes, gateway, and the ingress. Many projects run in production on Google Kubernetes Engine, and companies partner with AWS, GCP, and Azure to provide training and assist in completing modules regarding portals.

    What do I think about the scalability of the solution?

    Google Kubernetes Engine is a scalable solution.

    How was the initial setup?

    The setup was confusing. We were able to install it locally, but it was slow.

    What about the implementation team?

    Because I worked in production, I did not use technical support directly. It was required but most likely very minimal.

    What's my experience with pricing, setup cost, and licensing?

    I do not know specific details about the price, but I believe Google Kubernetes Engine is cheaper than Pivotal Cloud Foundry.

    Which other solutions did I evaluate?

    We compared Google Kubernetes Engine with Pivotal Cloud Foundry and found that Google was better.

    What other advice do I have?

    I rate this solution seven out of ten. I believe Google Kubernetes Engine is the best solution in the market.

    Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
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    PeerSpot user
    Head of .NET Department at Evozon Systems
    MSP
    Top 5Leaderboard
    It's a good solution for orchestrating Docker containers depending on your technology stack

    What is our primary use case?

    Google Kubernetes Engine is used for orchestrating Docker containers. We have 30 or 40 customers working with this solution now. We'll probably see 10 to 15 percent growth in the number of customers using Google Kubernetes Engine in the future. 

    For how long have I used the solution?

    We've been using Kubernetes Engine for five years.

    What do I think about the stability of the solution?

    Google Kubernetes Engine is less stable in some highly complex deployments with many nodes. However, those are probably edge cases.

    What do I think about the scalability of the solution?

    Google Kubernetes Engine is scalable 

    How was the initial setup?

    The ease of deployment for Google Kubernetes Engine is about average.  

    What's my experience with pricing, setup cost, and licensing?

    Google offers yearly and monthly subscriptions. 

    Which other solutions did I evaluate?

    We don't prefer Google over Amazon or any other Kubernetes solution. It depends on the technology stack that our customers choose based on project needs. It's not better than the others.

    What other advice do I have?

    I rate Google Kubernetes Engine eight out of 10. 

    Disclosure: I am a real user, and this review is based on my own experience and opinions.
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    Dhananjay-Hiremath - PeerSpot reviewer
    Product Manager at Dell EMC
    Real User
    Top 20
    Economical solution that's great for microservices

    What is most valuable?

    Google Kubernetes Engine's most valuable features are microservices and its acquisition rate, which is very useful for scaling perspective.

    What needs improvement?

    The user interface could be improved. In the next release, I'd like to see better notifications.

    For how long have I used the solution?

    I've been using Google Kubernetes Engine for a few years. 

    What do I think about the stability of the solution?

    Kubernetes is stable because it has scalable architecture, so if any part goes down, it starts from the other parts. 

    What do I think about the scalability of the solution?

    Kubernetes is scalable.

    How was the initial setup?

    The initial setup was simple.

    What's my experience with pricing, setup cost, and licensing?

    I would rate Kubernetes' pricing four out of five.

    Which other solutions did I evaluate?

    I also evaluated VMware, which is more expensive than Kubernetes.

    What other advice do I have?

    I would give Kubernetes a rating of eight out of ten.

    Which deployment model are you using for this solution?

    Hybrid Cloud
    Disclosure: My company has a business relationship with this vendor other than being a customer: Partner
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    Chris Njuguna - PeerSpot reviewer
    Founder at Ishara Data Solutions Ltd
    Real User
    Top 20
    Great solution for container deployment

    What is most valuable?

    Google Kubernetes Engine's most valuable feature is container deployment.

    What needs improvement?

    An area in which Google Kubernetes Engine could improve is configuration.

    For how long have I used the solution?

    I've been working with Google Kubernetes Engine for a few months.

    What do I think about the stability of the solution?

    Google Kubernetes Engine is stable.

    What do I think about the scalability of the solution?

    Generally, Google Kubernetes Engine is easy to scale, but it can be difficult in some cases.

    How was the initial setup?

    The initial setup is generally straightforward but can become a bit difficult with more complex projects.

    What's my experience with pricing, setup cost, and licensing?

    The price for Google Kubernetes Engine could be lower - I'd rate its pricing at three out of five.

    What other advice do I have?

    I'd rate Google Kubernetes Engine as eight out of ten.

    Which deployment model are you using for this solution?

    Private Cloud
    Disclosure: I am a real user, and this review is based on my own experience and opinions.
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    Buyer's Guide
    Download our free Google Kubernetes Engine Report and get advice and tips from experienced pros sharing their opinions.
    Updated: September 2022
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    Buyer's Guide
    Download our free Google Kubernetes Engine Report and get advice and tips from experienced pros sharing their opinions.