Google Compute Engine Reviews & Product Details

I like that it has many tools to deploy in a short time. Review collected by and hosted on G2.com.

For some things, you need to implement other things, but the process isn’t clear, and it takes time to understand it and fix the problem. For example, when you need to enable security. Review collected by and hosted on G2.com.

Google Compute Engine is a good option for anyone who needs a virtual computer system to run a wide range of computing applications and projects. I especially like the broad selection of instances to choose from, as well as how easy it is to set up and manage using both the GUI and the command-line interface. I am able to setup my instances quickly at a cost that is not too prohibitive. Their multimodal AI Gemini, is also integrated for those who need it, I personally did not need it. Review collected by and hosted on G2.com.

I dislike the fact that many times I am denied access to higher performance gpu compute to run my AI intensive applications mainly because they are reserved for larger organizations . Review collected by and hosted on G2.com.

There are a few things which I personally felt nice about using google compute engine,they are like easy to scale and deploy the application near to users having its low latency global network, one more thing which caught my attention was its capability to run vm instances from one host to another without downtime as it not only required for stability but also when we maintain or health check our applications. Review collected by and hosted on G2.com.

I think it's about challenge which team needs to figure out which I experienced are like its no.of data centres are less which sometimes impact latency and performance for users, another challenge would be its complex pricing structure which needs to be made simpler for user to understand clearly. Adding to this,one more challenge would be its limited support system. Review collected by and hosted on G2.com.

I really like how Google Compute Engine gives me full control over the virtual machines. I can choose CPU, memory, OS, and storage based on my workload without any restrictions. Scalability is strong too; I can start small and scale up instantly as my workload grows. This is important for handling large geospatial data and machine learning pipelines. The performance is reliable, and instances remain stable even under high processing loads, which is great for long-running jobs. Communication is smooth as well, and it works well with the rest of Google Cloud, like storage, BigQuery, and AI tools. It especially helps with GIS and data pipelines by removing hardware limitations and giving me speed and control. Review collected by and hosted on G2.com.

Some areas need improvements. The setup is not beginner-friendly, like dealing with networking and IAM slows me down when I just want to run a quick job. The cost visibility can be confusing, and billing is granular. Small mistakes like leaving instances running can increase costs fast, and alerts need manual setup. The cost management overhead means I still manage VMs, patch, monitor, and optimize, rather than it being fully handled like serverless. GPU and quota limits can take time to sort out and block fast experimentation. Overall, it's powerful but not simple, and I need cloud experience to use it efficiently. Review collected by and hosted on G2.com.

I like the integration of Gemini with Google Compute Engine, as it allows me to create a markdown file on my server, download it with the training data, and let Gemini handle the scripting. This integration makes my tasks a lot easier and faster. I'm also looking forward to trying the Google Drive integration for longer training sessions because of the time limit, which seems promising for my workflow. Review collected by and hosted on G2.com.

I would be happier if the time limitation could be longer, because it's not even 12H. I got about 1H and then it told me to stop because of high demand, so after that I could only use CPU, but it would take forever to train the model on that. Maybe the UI is a little bit messy. When you have lots of scripts, you have to scroll up or down and search for the training script etc. This could use an upgrade. Review collected by and hosted on G2.com.

I love how practical and easy to use it is; it has really improved the way I manage my VMs. It gives me plenty of options to create and configure different settings for some of my older services running there. The integration with the Google CLI is great and gives me more control. I also appreciate the pricing best practices it supports, like shutting down machines at night or setting different schedules. Review collected by and hosted on G2.com.

Between AWS EC2 and GCE, AWS EC2 used to have the worst documentation out there, but now GCE takes first place. Review collected by and hosted on G2.com.

I really like how I can start a Google Compute Engine instance by clicking on the SSH button that is available on the portal, so I don't need to hassle with installing gcloud or SSH to my own terminal. It is a really handy feature. I really like its user interface—everything I need is visible. For example, if I need an external IP, it's already visible in the table at the top. It doesn't waste time; everything is just right there available on the screen. The initial setup was really smooth because of the SSH button. Review collected by and hosted on G2.com.

I can't see or update anything of backend files if the person responsible is on leave or if the backend suddenly breaks. I wish there was a way to control access so any developer can decide who else can access their folder. Review collected by and hosted on G2.com.

One thing I really like about Google Compute Engine is how much control it gives you over your virtual machines. You can choose the exact CPU, memory, disk type, and networking configuration depending on what you need. It’s very flexible compared to some higher-level managed services.

In my day-to-day work I also appreciate how well it fits into automation workflows. Spinning up instances, attaching disks, or modifying infrastructure is easy through APIs or infrastructure-as-code tools. Once everything is defined properly, you can recreate environments pretty quickly.

Overall it feels reliable and production-ready, which is really important when you’re running backend services. Review collected by and hosted on G2.com.

The pricing model can take some time to fully understand. There are a lot of small components that affect the final cost, like machine types, storage, and network traffic. If you’re not paying attention it’s easy to underestimate how much something will cost.

Another minor downside is the cloud console. It’s powerful but sometimes feels cluttered, especially when you’re trying to find a specific configuration option. After using it for a while it becomes easier, but the first impression can be a bit overwhelming. Review collected by and hosted on G2.com.

I used Google Compute Engine to run microservices and test new ones. I really like that you can manage infrastructure with code, which allows for quick automation of server deployments and configurations through scripts. Scaling infrastructure as load increases is extremely convenient. I especially appreciate the flexible orchestration in GKE and the quick configuration of VM Instances. The monitoring also impressed me. Integration with the load balancer and the ability to quickly deploy a virtual server without setting up physical hardware greatly simplifies work. Review collected by and hosted on G2.com.

Sometimes the interface can be a bit complex for beginners because there are many configurations and components. Also, the pricing model is not always immediately clear, as the cost is composed of several parameters - computing resources, network, disks, and other services. I would like a simpler and more visual cost calculation for new users, and of course, discounts for different categories of users. Review collected by and hosted on G2.com.

One of the things I like most i like about Google Engine is that I can create highly customized machine types with exact CPU and memory specs based on the specific workloads. The live migration is also a massive advantage, as it keeps my instances running smoothly even during backend maintenance. Review collected by and hosted on G2.com.

While the core features are robust, I find that some of the documentation are outdated or lacks in-depth troubleshooting steps. I also find that the premium tech support cost can be quite high for smaller organizations that aren't on an enterprise level. Review collected by and hosted on G2.com.