We simply can’t talk about container orchestration without Kubernetes, so why should Machine Learning (ML) and Artificial Intelligence (AI) be far when it comes to improving this excellent project!

That is exactly why today we bring you some of the best Artificial Intelligence (AI) and Machine Learning (ML) tools that will help a lot with Kubernetes resource optimization.

This is the perfect blog for you if you are into AI advancements and if you like the idea of integrating Artificial Intelligence (AI) into your software development process.

We are going to introduce a few powerful tools for you that will help you improve efficiency as well as reduce costs dramatically as well.

We would also love to know your suggestions regarding other tools that you want to be included in this blog so that we might be able to update this blog whenever a new tool comes out.

So, let us begin this journey of finding the best tools for Kubernetes Resource Optimization.

Best tools for Kubernetes Resource Optimization



If you are looking for a tool with the best kind of cost management and optimization insights then this is the best option for you.

Kubecost helps you with everything from Kubernetes clusters and the best parties you can find out where exactly your resources are going. This means you can keep track of everything right up to deployment, namespace and even up to the pod level.

This is possible because Kubecost is actually using ML algorithms in order to find historical and real-time current usage data.

Important Features

Kubecost has a lot of good features but some of the best features include a cost analysis and allocation features that help to break down all the costs and get a great idea about where your resources are going.

You also have things like budget alerts and that’s self-explanatory along with the right sizing recommendations which help you get recommendations for adjusting resource requests and this helps eliminate waste.

Along with that you also have things like customisable reports and you can actually tune in the factors to your specific needs so that you can get a custom report of everything you want to see.


While you can use Kubecost for a lot of things the best thing you can do with it is budget tracking because setting alerts for your project expenditures can really pay off especially if the project is a little bit tight on the budget.

You can also do things like capacity planning because if you want proper scalability and deficiency with rising demand then this is a very good thing to use.

Along with that you also have things like over-provisioning detection to track over-provisioned resources.


There are a few problems with this as well because you might face small inefficiencies that might not seem significant but if you add them up then they can cause serious issues.

The other thing you must keep in mind is that it is not always good to trust automated suggestions all the times.

There must be manual observation as well.



The second tool we are going to talk about is definitely going to be Magalix because of its amazing set of features that basically turns Kubernetes smart for you.

We are talking about features that allow automatic resource optimisation and the best part is that its machine learning models can actually analyse your cluster’s behaviour so that you get the best kind of recommendations.

These recommendations come in all shapes and sizes regarding configuration and research allocation as well as scaling and much more.

Important Features

We can talk about a lot of features but the features that you should definitely not miss out on are going to be AI-driven recommendations because that’s where the magic happens regarding actionable insights for improving performance.

You also have things like autonomous optimisation which basically allows it to do everything and you do not even have to monitor anything real-time or enable anything.

Then there is multi-cluster management and it basically means you can take care of multiple Kubernetes clusters from one console and also get a view of everything.


You should use Magalix for things like dynamic workload scaling because instead of manually adjusting resources in order to match demand, this will allow you to basically automate workload scaling so that there are no fluctuations in traffic and you also get the best cost efficiency with this.

You also get multi-cluster optimisation which means you will be able to manage multiple clusters and also get centralised insides as well as the best kind of recommendations across different environments.

Along with that you also get cluster compliance which means if you use Magalix then you are going to be alright with any kind of industry and policies and compliance checks.


However, nothing is really 100% perfect and there is a same with this tool because the optimization and automation that it provides might be good for now but the problem lies with the users.

If there are changes made in the future such as usage patterns then it does not account for those changes.

One of the other important things you must consider is that every application has its own architectural needs as well as performance needs but the optimisations provided by Magalix might not be the best fit always.



If you are looking for a tool that is going to help you fine-tune applications that also keeps performance in mind as well as resource utilisation then StormForge is the ultimate solution.

The best part about this tool is that it helps you set parameters for the best kind of resource efficiency and that is why it is trusted throughout the industry and can even be called an essential tool when it comes to AI and Kubernetes.

Important Features

One of the most important features that you should never miss is going to be the ML optimisations because machine learning is the only way to actually understand your configurations and automate the process.

You also get automatic performance testing so that it can find out pitfalls and make corrections automatically which means you are going to get the best kind of performance every time.

If this sounds a little bit complicated then you do not have to worry because the user interface of StormForge is actually quite easy to understand and can help you with decision-making quite easily.


You should definitely use it for adaptive scaling and this comes in handy when you have ups and downs in the load and it can balance them out and can be really handy in the real world when it comes to cost efficiency.

There is also continuous performance enhancement which means it automatically tracks the performance how the application and does ongoing optimisations for the best-case scenario.

We would also suggest you stay in touch with its vast community and you are going to get a lot of benefits from all the collective knowledge.


However, that’s not to say that there are no problems with people using StormForge. One of the mistakes people make is that they over-optimise the tool and that actually makes it counterproductive.

This is because you need to have a balance between manual intervention and tuning and automation because there are some patterns that only you can understand.

The other problem is that sometimes the tool is just not enough for the very specific needs of your applications.



If you are looking for a tool with the best kind of cost trimming as well as workload analysis and even optimisation without cutting down on performance then CAST AI is the best option for you.

It has some amazing features that allow it to do multi-cloud deployment as well as hybrid cluster management.

Important Features

One of the most important features that set this tool apart from anything else is definitely its unique machine-learning algorithms that allow for the best kind of workload analysis.

Along with that you also get a lot of cost trimming as well as performance improvement notifications and reminders and these happen in real time.

Additionally, you also get multi-cloud deployment which is always a good thing.


One of the best ways you can use CAST AI is when you want to do cost optimisation in complex environments.

Along with that, you can also use this very helpful tool when it comes to cost management across hybrid clusters and multiple cloud environments.


That’s not to say that there are no problems because there are some pitfalls to it and one of them is that if you do not implement the recommendations timely then there is no use of using this tool.

Along with that you also need to have a good monitoring system to address cost inefficiencies when it comes to complex environments.



Kubeflow is a very unique tool that allows ML pipelines to orchestrate complicated workflows on Kubernetes.

The best thing about this tool is that if you want to implement machine learning systems and scale them up and also support portability then this is the best way to do it.

Important Features

One of the most important features when it comes to Kubeflow is automation and portability whenever you want to integrate machine learning.

Along with that there is also support for data processing as well as data training and is very helpful when it comes to ML model deployment.

Additionally, you also get easy integration with a lot of ML libraries as well as frameworks.


If you have to do anything with machine learning in Kubernetes then this is the best tool for whether it is managing models are processes or even deploying them.

Additionally, this tool is excellent for machine learning task automation and can do it very efficiently and improve your productivity.


If you talk about the problems then one of the most common problems is definitely neglecting resource utilisation optimisation when it comes to machine learning workflows.

One of the other things to keep in mind is definitely failure to integrate popular machine learning frameworks that can dramatically improve productivity.


Let’s not talk about an open-source project that is actually one of the most popular tools in Kubernetes.

We are of course talking about Alameda. This is the best tool if you want to integrate AI to forecast application workloads and it also allows you to automatically adjust different resources in the best way possible.

Important Features

Nothing can beat Almedia’s predictive auto-scaling and we know how important it is to optimise workload demands and having a tool that lets you do this automatically is excellent.

We also have to talk about the very important and popular feature known as the anomaly detector which helps you identify points of stress as well as unusual patterns that might become an issue in the future.

You also get capacity planning and that helps you get access to even more data so that your investment decisions are made correctly.


You can use Almedia for a lot of things whether it is predictive scaling for eCommerce platforms or whether you want efficient resource allocation in different kinds of financial services.

This is also an excellent tool if you want to do healthcare data analysis and can be quite efficient in dealing with different kinds of data-intensive healthcare applications.


There are problems as well and these are some mistakes people make. One of the most common mistakes is that people become very reliant on predictions and while this tool generally provides good predictions but you should never rely on predictions too much.

The other thing is that you must always have human oversight over automated systems and there is something people sometimes forget when the tool is really good.

We hope this blog helps you find out the best machine learning tools to utilise with Kubernetes. And if you are someone who is looking for professional Kubernetes developers then we are here for you.

We are Think To Share IT Solutions and we are the eminent experts when it comes to AI integration into everything including Kubernetes.

We will help you streamline your operations and improve efficiency as well as bring cost-savings to your business with the help of our IT Solutions.

We welcome you to visit our website and check out everything we do.