Managing Partner, Co-Founder at Cloud Analytics Sdn Bhd
Real User
Top 20
2024-09-26T07:37:00Z
Sep 26, 2024
The main advantage of Google Compute is their private network, flexibility of clusters, and scalability. Their pricing is also competitive compared to other major players like AWS and Microsoft Azure. I'd rate the solution eight out of ten.
I have never used AI in Google Compute Engine. I recommend the tool to others since it has high availability features, scalability, and stability. I rate the tool an eight out of ten.
First of all, you have to analyze your workload and sign which kind of machine type, how much memory you need, and what kind of traffic you expect. Analyze your workload and your requirements. Make a cost explanation of how much you can spend for your service in a month. Once you analyze your workload and determine your cost explanation, use the pricing estimation with the price target. And because you are the amount you plan to spend in a month. Find the cost provided by the price package. Compare both. If there is a minor or no difference, both are comparable. Everything is matching, you can go with your requirements and build your infrastructure based on your economy. If there is a huge difference, you can reassess your resources in pricing guidelines and make resource changes. But the things you have to come there. If there is no significant difference, you can proceed. Otherwise, change the option. And then you will get a solution, how to make both things like we've both things, like the infrastructure, it's preferred in your pricing estimation, and your actual costs. If both are almost similar, you can create it, use it, and deploy it. Overall, I would rate the solution a nine out of ten.
Network Engineer at a tech services company with 51-200 employees
Real User
Top 20
2023-09-20T07:42:40Z
Sep 20, 2023
As long as you stick to the actual web console, the GUI, you can use it even if you have little understanding about what is sectionally going on. Overall, I would rate the solution a seven out of ten. It's pretty customizable; for example, when you are using the CLI, you can get pretty much anything done. But I didn't give it a ten because it has some limitations. For example, you don't get through layer two connectivity. So, I've had some difficulty deploying custom VMs. For example, you can't deploy a KVM file to file directly on GCP.
My advice to new users would be to hire a good data engineer since it's only user-friendly for experienced users. They should also think about what is the main trigger for moving to cloud and if they have a precise plan for doing that. Using Compute Engine is just the first step. After, you can decide whether you will migrate your database to cloud or not. I recommend having at least one data engineer and a solution architect to help create a plan and reach targets. Overall, I would rate it an eight out of ten.
I would suggest new users take a look at Spot instance since it can help you significantly reduce costs. I would like to see dedicated and better UX for container deployment in the tool’s future releases. Google Compute Engine is easy to use and has great authentication management for Google APIs. The solution can directly escalate and it's easy to get started with.
IT Support at a tech services company with 51-200 employees
Real User
2019-09-29T07:27:00Z
Sep 29, 2019
The advice that I would give to someone considering this solution is that you have to take a close look at what your costs are, where you want scalability and consider the reliability issues because it's not always obvious where . There are a number of places where commercial cloud applications work very well. There are places where it doesn't. For applications where you have large dynamic changes in load and storage, the cloud environment can be great because you pay for what you use and can take advantage of running up resources on demand. For static environments where you've got a reasonable amount of infrastructure, a cloud solution can be highly expensive and no more reliable.
Google Compute Engine delivers virtual machines running in Google's innovative data centers and worldwide fiber network. Compute Engine's tooling and workflow support enable scaling from single instances to global, load-balanced cloud computing.
Compute Engine's VMs boot quickly, come with persistent disk storage, and deliver consistent performance. Our virtual servers are available in many configurations including predefined sizes or the option to create Custom Machine Types...
The main advantage of Google Compute is their private network, flexibility of clusters, and scalability. Their pricing is also competitive compared to other major players like AWS and Microsoft Azure. I'd rate the solution eight out of ten.
I have never used AI in Google Compute Engine. I recommend the tool to others since it has high availability features, scalability, and stability. I rate the tool an eight out of ten.
First of all, you have to analyze your workload and sign which kind of machine type, how much memory you need, and what kind of traffic you expect. Analyze your workload and your requirements. Make a cost explanation of how much you can spend for your service in a month. Once you analyze your workload and determine your cost explanation, use the pricing estimation with the price target. And because you are the amount you plan to spend in a month. Find the cost provided by the price package. Compare both. If there is a minor or no difference, both are comparable. Everything is matching, you can go with your requirements and build your infrastructure based on your economy. If there is a huge difference, you can reassess your resources in pricing guidelines and make resource changes. But the things you have to come there. If there is no significant difference, you can proceed. Otherwise, change the option. And then you will get a solution, how to make both things like we've both things, like the infrastructure, it's preferred in your pricing estimation, and your actual costs. If both are almost similar, you can create it, use it, and deploy it. Overall, I would rate the solution a nine out of ten.
As long as you stick to the actual web console, the GUI, you can use it even if you have little understanding about what is sectionally going on. Overall, I would rate the solution a seven out of ten. It's pretty customizable; for example, when you are using the CLI, you can get pretty much anything done. But I didn't give it a ten because it has some limitations. For example, you don't get through layer two connectivity. So, I've had some difficulty deploying custom VMs. For example, you can't deploy a KVM file to file directly on GCP.
I rate the product a ten out of ten.
My advice to new users would be to hire a good data engineer since it's only user-friendly for experienced users. They should also think about what is the main trigger for moving to cloud and if they have a precise plan for doing that. Using Compute Engine is just the first step. After, you can decide whether you will migrate your database to cloud or not. I recommend having at least one data engineer and a solution architect to help create a plan and reach targets. Overall, I would rate it an eight out of ten.
I would suggest new users take a look at Spot instance since it can help you significantly reduce costs. I would like to see dedicated and better UX for container deployment in the tool’s future releases. Google Compute Engine is easy to use and has great authentication management for Google APIs. The solution can directly escalate and it's easy to get started with.
I rate Google Compute Engine 10 out of 10.
I rate Google Compute Engine a nine out of ten.
The advice that I would give to someone considering this solution is that you have to take a close look at what your costs are, where you want scalability and consider the reliability issues because it's not always obvious where . There are a number of places where commercial cloud applications work very well. There are places where it doesn't. For applications where you have large dynamic changes in load and storage, the cloud environment can be great because you pay for what you use and can take advantage of running up resources on demand. For static environments where you've got a reasonable amount of infrastructure, a cloud solution can be highly expensive and no more reliable.