Share your experience using Mirantis Kubernetes Engine

My use case for Microsoft Azure Container Service is for creating applications for internal use; it could be security-related or a service of some sort. The source code might be available in GitHub, and then I would publish the application securely to my internal customers.

The functions or features in Microsoft Azure Container Service that I find most valuable are the flexibility to deploy instantly, the monitoring of the applications, and the integration with Defender. I find security features and monitoring very useful. The main benefits Microsoft Azure Container Service provides for me are that it is easy to configure, easy to deploy, and functional. It definitely saves me money and time because it is all in one place, so that is also very positive.

Sometimes the integration could be better between the Azure environment; there is some tweaking needed when building Docker files and creating mount points, and it is not always that easy. From a GitHub source code environment, there can be vulnerabilities or hard-coded secrets that need addressing. If I configure my Docker image, I could still find issues in my source code that need fixing, so from that perspective, it is sometimes a bit cumbersome.

I have been working with Microsoft Azure Container Service for a year.

I would rate the stability of Microsoft Azure Container Service a ten.

The ability to scale and expand in Microsoft Azure Container Service is good, and I would also rate it a ten.

Technical support from Microsoft is very good; the way they have created support tickets integrated into the portal is really good, and I am really surprised with that. I am satisfied with the response time; it is usually within six to eight hours or so, which is still good as things are not so critical.

The initial setup process for Microsoft Azure Container Service is simple.

I have compared Microsoft Azure Container Service with a product from AWS. Comparing the two vendors, it is a bit complex because I do not have access in AWS and I cannot set up components or services there. But in Azure, I do have access, so I picked Azure to run the service.

I am using App Containers and Container Instances or Container Applications. I am using a Key Vault, and I am working with services like Azure File Storage, Azure Stack, and App Services, and I use App Container and Container Instances. I do use Azure Kubernetes Services for CI/CD capabilities. Regarding Azure Kubernetes Services integration with Azure DevOps Services, sometimes it could be complex, but it works fairly well with DevOps. I try to use Azure native monitoring for monitoring tools in AKS. Monitoring logs and so on work well for me. Some parts of my organization use extra tooling, for instance, DataDog or those kinds of monitoring services, but it is not really required.

When it comes to purchase processes for Microsoft solutions, I am directly working with the vendor. For pricing regarding Microsoft Azure Container Service, I would rate it a six, where one is high and ten is low. Sometimes the integration could be better between the Azure environment; there is some tweaking needed when building Docker files and creating mount points, and it is not always that easy. I would rate this review a nine overall.

My primary use case for Spot revolves around managing and optimizing my compute infrastructure across AWS, mainly EC2 and EKS on a daily basis, such as monitoring the workloads and ensuring that Kubernetes and EC2 instances are provisioned, scaling, and replacing spot instances for ephemeral tasks.

Recently, I have been using Spot for Kubernetes through Ocean, which continuously analyzes pod capacity requirements and adjusts the node groups accordingly. It helps handle node drains, rebalancing, and spot interruption gracefully without requiring custom automation. I have been using it recently for provisioning ephemeral tasks for applications such as stateless applications.

Another main use case for Spot revolves around managing ephemeral tasks or stateless application workloads. Additionally, I focus on cost optimization and optimization of my overall work nodes so that I do not need to manage everything or the nodes for my workloads.

Spot overall is a really good tool, and the best feature is the intelligent autoscaling engine it has. It looks at pod requests, historical usage, interruption risk, and pricing trends to choose the best instance type at the lowest cost. The fact that it can mix dozens of instance families dynamically is something which the AWS native autoscalers simply cannot do. I also love the zero-downtime node replacement.

Spot's zero-downtime mode replacement has helped my team by enabling us to reduce our operational overhead. I no longer maintain custom scaling scripts, which has saved several hours or weeks of SRE and DevOps time.

Spot has impacted my organization positively by delivering a very clear ROI. My compute costs have reduced, capacity and production output have increased, and my overhead for maintaining custom scripts or doing some of the tasks manually has been saved. It has saved significant time and resources and given my organization a better competitive edge.

I think overall Spot is quite good. The UI is powerful, but it can feel a bit dense for some new users. More guided onboarding would help teams adopt advanced features faster, and deeper insights into Kubernetes resource usage would be beneficial. Overall, it is a really good platform that is quite mature and stable.

I chose a rating of nine because it may sometimes be a bit overwhelming for newcomers, and there are also a few areas in which the EKS Kubernetes level granularity is a little missing. Overall, I think Spot is a really good and stable tool.

I have been using Spot for two years.

Spot is stable and quite mature.

Spot's scalability is quite good, and it can be expanded to multiple environments.

The customer support is quite good. I did not need to reach out to them a lot, but I think it was good.

Positive

Previously, I was using native solutions only.

Before choosing Spot, I evaluated other options. I was totally dependent on native tools, but I assessed some alternatives such as Cast AI, which was a combination of a few tools including Cast AI and Carpenter for EKS. However, I chose Spot because it provided more automation, smarter instance selection, and had features of many other alternative tools combined in one.

I purchased Spot through the AWS Marketplace.

I cannot share the exact number, but I can share the figure that my EC2 and EKS compute cost reduced by around 65 to 75% with Spot. This was because I was able to reduce some workloads for stateless applications, which needed ephemeral storage for running tasks and processing. This has helped me a lot because my overall operational overhead has been reduced significantly, and I save a lot of time from otherwise managing the servers or maintaining everything properly.

I have seen a return on investment as I was able to reduce my compute cost by 60 to 65% for some of my applications that needed light temporary processing tasks with ephemeral storage and stateless applications. It has also reduced some of my overheads because I do not need custom scaling scripts. It has saved me several hours a week for my key SREs and my DevOps teams.

The experience with pricing, setup cost, and licensing has been quite good. The pricing is reasonable and convenient, and the value it offers is completely in line with what I am spending. I think this is really good. However, the exact licensing part is handled by some other members of my team.

I would definitely recommend Spot for those looking to run their workloads on AWS, especially those needing cheaper compute for tasks that will not require much for temporary processing. For scaling up and down existing applications, Spot is really one of the best investments you can make, especially for EKS. I gave this product a rating of nine out of ten.