Magalix is your agent that keeps an eye on 10s of data points and configurations inside your Kubernetes clusters. Magalix makes sure that your cluster and applications are reliable, efficient, performant, and secure. You will continuously get recommendations to improve your cluster and applications readiness for production. Our Autopilot feature automates some of the changes for so that you focus on your core stuff.
Kubernetes is a great platform to automate a lot of infrastructure and application work. However, it is quite complex to configure and reach the ideal automation of everything inside it. New features are introduced every few weeks, which are hard to keep up with. Also, continous changes that your team might be pushing to your infrastructure and applications compound that complexity. Magalix helps you to move much faster with your automation and keep you always on top of it. For example, having the right observability pipeline requires many components to be installed, configured and maintained, such as Prometheus, Grafana, and apps exporters. Magalix will give you a detailed observability analysis and recipes to improve that pipeline whenever possible. Also, you might be missing some critical configurations to have reliably deployment mechanism of your containers. Magalix will provide recommendations to tweak your deployment policies. Magalix autopilot will automate some dynamic recommendations, such as managing the scale and performance of your applications.
We keep adding advisors to cover the critical areas inside Kubernetes. Magalix currently covers four main areas:
- Performance by continuously watching throttled containers/apps and recommending improvements.
- Utilization by comparing used resources with the available capacity to reallocate them based on variable workloads.
- Cost Optimization by suggesting changes at the VM level to save money in case of cloud infrastructure or identify the best configurations if you are running Kubernetes on-prem.
- Reliability by observing health probes, deployment configurations, etc.
- Observability through a detailed analysis of the official cloud-native monitoring pipeline.
Your Inputs Are Welcome
We keep adding more checks and automation. If you are looking for a particular check and its automation, please drop us a line.
Magalix currently automates performance and utilization management. When you turn-on the Autopilot, Magalix agent continuously scale applications for performance, utilization, and reliability. Some areas require application-specific logic that we only point out for you, such as implementing the Liveness probes.
No. Magalix runs as a read-only companion. No changes will be automated until you explicitly enable them yourself.
Magalix uses the best Kubernetes design practices and recommended cloud-native configurations across all the possible configurations, such as resources management, configurations, deployments, security, monitoring pipelines & observability. We keep observing best practices there and add accordingly necessary checks. You will see inside each recommendation the standard we used.
The current Kubernetes management tools of abstract and automate the basics of infrastructure management. But they don't provide any insights about improving your DevOps practices to gain the best performance, utilization, reliability, and security inside your cluster. You will also face many knowledge gaps inside your team. Not all your team members will be able to identify and implement the best cloud-native patterns and practices. Magalix is a guiding tool to help you cover those gaps that can cost you downtime in your production environments.
No. Magalix works side by side with any Kubernetes version. It is compatible with GKE, AKS, EKS, and many other managed Kubernetes. You will get recommendations and checks regardless of where Kubernetes is running. There might be some restrictions though based on the platform, such as cost optimization inside on-prem Kubernetes clusters.
The main idea behind Magalix is to recommend improvements and automate them for you. Below is a high-level diagram to explain how Magalix works to observe and automate some recommendations inside a Kubernetes cluster. To kick-start, this workflow, Magalix agent pod must be installed at the target cluster. The agent performs four main tasks: (1) Sends resources metrics to the backend, (2) Executes scalability decisions, and (3) provide relevant feedback data to Magalix BE. For more detail about the Magalix end-to-end autoscaling workflow, please read the detailed description here.
Magalix high-level workflow
- Magalix system and components do not have access to any of your application's data or code. Our system works at the operating system level to read and analyze configurations.
Updated 4 months ago