AWS Auto Scaling monitors applications and automatically adjusts capacity to maintain steady, predictable performance at the lowest possible cost. The vendor states that using AWS Auto Scaling, it’s easy to setup application scaling for multiple resources across multiple services in minutes.
N/A
Mirantis Kubernetes Engine
Score 9.4 out of 10
N/A
The Mirantis Kubernetes Engine (formerly Docker Enterprise, acquired by Mirantis in November 2019)aims to let users ship code faster. Mirantis Kubernetes Engine gives users one set of APIs and tools to deploy, manage, and observe secure-by-default, certified, batteries-included Kubernetes clusters on any infrastructure: public cloud, private cloud, or bare metal.
$0
per year
Pricing
AWS Auto Scaling
Mirantis Kubernetes Engine
Editions & Modules
No answers on this topic
Free
$0.00
per year
Basic
$500.00
per year
Offerings
Pricing Offerings
AWS Auto Scaling
Mirantis Kubernetes Engine
Free Trial
No
Yes
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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These pricing options are compatible with Linux or Windows Server and are per year, per node. The basic version requires maximum online purchase not to exceed 50 nodes. Support/professional services are not included.
1. When down time needs to be eliminated. 2. Have templates that can be used for future application deployments. 3. Templates can serve as a documentation for keep track of server configuration. 4. Handling of high usage periods like reporting season.
Docker is great for when you would want to use a VM for any given application, but don't need the overhead of the whole OS. Docker containers use very little computing resources, boot up very quickly, and are very easy to set up. An instance where Docker may not be appropriate would be for an application that requires good security. If in this situation, a true VM would probably be your best bet.
Docker has a bit of a learning curve, and it takes some time to become familiar with the tooling and syntax. Transitioning an existing architecture to docker can represent a significant investment.
Docker attempts to provide some level of cross-host container orchestration via swarm, but it falls short of third-party solutions like kubernetes.
We occasionally run into stability issues when the docker daemon is subjected to high load (many applications starting/stopping frequently). In these cases, docker hangs and we have to restart or replace the node.
We use AWS auto scaling for scaling up our cloud virtual machines to handle the increase or decrease in workload. It really helps us to satisfy the demand because it doesn't take lot of time to spin up new machines. I gave the rating 10 because it really does help you to handle the sudden spike in number of requests.
Docker's CLI has a lot of options, and they aren't all intuitive. And there are so many tools in the space (Docker Compose, Docker Swarm, etc) that have their own configuration as well. So while there is a lot to learn, most concepts transfer easily and can be learned once and applied across everything.
The community support for Docker is fantastic. There is almost always an answer for any issue I might encounter day-to-day, either on Stack Overflow, a helpful blog post, or the community Slack workspace. I've never come across a problem that I was unable to solve via some searching around in the community.
AWS auto scaling handles the sudden surge in demand very effectively and it is also very cost effective in terms of pricing compared to the other service providers I have used. That is the main reason I opted for AWS auto scaling and also it is very reliable and has less fault tolerance.
I have not used any other software as a container management solution. Its containerized apps allow the usage of less memory, thus they start and shut down very fast. This tool is helping the enterprise software to work quickly against the changing conditions thus offers great scaling by simultaneously allowing me to meet the demands, which also leads to easy implementation of the strategies.
We are able to try things very quickly compared to before. If you need to debug it, changes on X/Y/Z will have an impact on the way your app works, and changing libraries or configurations of the environment easily can improve your development cycles.
In case someone new arrives, the onboarding is pretty easy thanks to Docker. We have tried many configs and images until we reached a point were we have what we want. We don't have to painfully do that again for every new user. We just send him the image.
