Google Cloud Run vs. Google Compute Engine

Microservices and RestFul API application as it is fast and reliant. Seamless integration with event triggers such as pubsub or event arc, so you can easily integrate that with usecases with file uploads, database changes, etc. Basically great with short-lived tasks, if however, you have long-running processses, Cloud Run might not be idle for this. For example if you have a long running data processing task, other solutions such as kubeflow pipelines or dataflow are more suited for this kind of tasks. Cloud Run is also stateless, so if you need memory, you will have to connect an external database.

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You can use Google Cloud Compute Engine as an option to configure your Gitlab, GitHub, and Azure DevOps self-hosted runners. This allows full control and management of your runners rather than using the default runners, which you cannot manage. Additionally, they can be used as a workspace, which you can provide to the employees, where they can test their workloads or use them as a local host and then deploy to the actual production-grade instance.

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• Auto scaling is the best one
• provide direct VPC connectivity and rigid network
• Cloud SQL and Pub/Sub services
• Handling latency issues

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• Scaling - whether it's traffic spikes or just steady growth, Google Compute Engine's auto-scaling makes sure we've got the compute power we need without any manual juggling acts
• Load balancing - Keeping things smooth with that load balancing across multiple VMs, so our users don't have to deal with slow load times or downtime even when things get crazy busy
• Customizability - Mix and match configs for CPU, RAM, storage and whatnot to suit our specific app needs

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• The UI can be made simpler. Currently the UI is bloated and it takes time to find out what you want
• More integrations with container registry providers (ECR, dockerhub)
• Better permissions UX. Currently GCP requires service accounts to be used with cloud products, the experience adding/removing permissions is difficult to navigate

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• 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.

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We definitely need to renew it because we dont own our own infrastructure and storage and we are happy with Cloud Run features

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Its pretty good, easy and good performance. Also, interface is very good for starters compared to competitors. Infra as Code (IaC) using Terraform even added easiness for creation, management and deletion of compute Virtual Machines (VM). Overall, very good and very easy cloud based compute platform which simplified infrastructure, very much recommend.

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The UI/console is great... the documentation is top-notch for developers, but the CLI itself when you have to script around it is very complex and easy to forget some options... the downside of a generic command line client.

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Having interacted with several cloud services, GCE stands out to me as more usable than most. The naming and locating of features is a little more intuitive than most I've interacted with, and hinting is also quite helpful. Getting staff up to speed has proven to be overall less painful than others.

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Not seen any major issues when we run applications its good

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Google Compute Engine works well for cloud project with lesser geographical audience. It sometimes gives error while everything is set up perfectly. We also keep on check any updates available because that's one reason of site getting down. Google Compute Engine is ultimately a top solution to build an app and publish it online within a few minutes

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Initially we felt slow but slowly it picked up and easy to manage

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It works great all the time except for occasional issues, but overall, I am very happy with the performance. It delivers on the promise it makes and as per the SLAs provided. Networking is great with a premium network, and AZs are also widespread across geographies. Overall, it is a great infra item to have, which you can scale as you want.

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• 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.

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I was involved in the initial implementation setup, Its easy with the given documentaiton we can do ourself. Not that critical

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AWS Lambda supports code zip package, enabling lower cold start time. Also, AWS Lambda pricing is much simpler, easier to understand.

Other than that, the 2 products are very similar, including the Docker image support: the image must be built based on proprietary base image.

Obviously, if your other services are running in GCP, then Google Cloud Run is your only choice for tight integration, & private networking.

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Google Compute Engine provides a one stop solution for all the complex features and the UI is better than Amazon's EC2 and Azure Machine Learning for ease of usability. It's always good to have an eco-system of products from Google as it's one of the most used search engine and IoT services provider, which helps with ease of integration and updates in the future.

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Not part of purchase.

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It has good auto scale feature and reliable also

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It works really well with other Google Cloud services, making it easy to build scalable solutions across different teams and locations.

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We have very good support when needed

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• Built in support for auto scaling helps reduce operational overhead
• Any application performance issues can be addressed quickly by allocating more resources while a proper fix can be planned and rolled out later
• Using Google Cloud Run enables development of microservices which provides granular control for scaling critical services in the platform

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• With Google Compute we don't have the overhead of managing our own data centers reducing costs and reducing the staff needed to manage systems.
• As I said earlier, Google's costs are ~1/2 of AWS, so we are able to see a ROI much faster.

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