Azure Kubernetes Service (AKS) vs. Google Cloud Run

AKS works very well for running containerized applications that require high availability and scalability. This includes systems like our HRIS platform and customer-facing web applications. AKS is a good choice when applications are broken into multiple services that need independent scaling and deployment. It provides the flexibility needed to manage these architectures effectively. But for single, low-traffic applications or simple internal tools, AKS can be overkill. For scenarios like that Azure App Service would be better.

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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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• AKS makes it easier to replicate data to multiple regions
• Azure portal make it easier to manage the resources of the organization

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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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• Steep learning curve
• Expected charges are unclear until you see real production usage
• Operations teams need to learn an entirely new skill set

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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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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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As already said, the UI/CLI and even terraform are perfectly fine, but certain details could be documented better. For instance, if I want to secure the whole Azure Kubernetes Service (AKS) with my own managed keys, then it is very complex and hard to get there. Not really a single source that gives you the whole picture. Besides that, it is still good to use, in most cases intuitive but details mentioned as above can be tricky.

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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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Not seen any major issues when we run applications its good

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

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Microsoft support was really good, whenever we raise any ticket they come back to us within a couple of hours.

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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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Amazon EKS stacked up very well and had better performance in some areas. However, Azure Kubernetes Service was a better fit given our Azure environment.

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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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Not part of purchase.

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It has good auto scale feature and reliable also

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We have very good support when needed

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• We had to spend more time on Azure Kubernetes Service (AKS) than on AWS and GCP to get our kubernetes cluster up and running
• The resources on nodes need to be left out unused, so effectively it is wasting money there
• It definitely made us spend more time into maintaining kubernetes

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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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