Containers are all the rage, and RedHat’s report1 made it very clear. 70% of the surveyed IT leaders confirmed that their organizations deploy Kubernetes, and almost a third shared plans to increase their usage of containers significantly.
Kubernetes as a container orchestrator ensures some automation, but if you’re looking to build a platform facilitating DevOps speed and application elasticity, there’s more work to be done.
For Kubernetes automation, CAST AI provides a better experience and incremental cost savings than internal DevOps resources and tools like native cluster autoscalers.
No matter if you manage Kubernetes on AWS/EKS, Azure/AKS, GCP/GKE, or AWS/kOps, CAST AI helps lower expenses and increase DevOps, CloudOps, and FinOps effectiveness.
Read on to discover how.
7 reasons to use Kubernetes automation with CAST AI
#1: Remove manual labor
With CAST AI managing your Kubernetes autoscaling, cost optimization, and monitoring across multiple zones and clouds, your DevOps team can significantly reduce the number of manual tasks.
This means they can spend less time manually scaling clusters, handling out-of-capacity cloud events, or guesstimating which instance types will be best considering availability and price across multiple zones.
CAST AI vs Native K8s Management
|Planning, monitoring, management|
|Scaling clusters||Requires DevOps resources and manual labor||Fully automated monitoring and auto-scaling|
|Out-of-capacity cloud events||Requires DevOps resources and manual labor||Fully automated monitoring and auto-scaling|
|Estimate instance types||Requires DevOps resources and manual labor||Fully automated monitoring and auto-scaling|
|Chose instance types based on price & availability||Requires DevOps resources and manual labor||Fully automated monitoring and auto-scaling|
|Multi-cloud deployment||Requires DevOps resources and manual labor||Fully automated monitoring and auto-scaling|
|Manage cloud budgets effectively||Requires DevOps to utlize native reporting and pricing tools to guess best configuration, which need to be revisited regularly||CAST AI cost visibility reports and available metrics give control and transparency to budget owners.|
|Reserved instances, Savings Plans||Requires DevOps to plan ahead, budget and commit, prone to create wasted resources due to seasonal fluctuations, or require on-demand resources at premium cost to deal with peak utilization cycles.||Dynamically adjusts allocation for maximum availabliilty and highest cost savings|
|Spot instances||Requires careful planning and DevOps on standby in the event of resources not being available with 2 minutes notice||Fully automated management of spot instances and dynamic transition to other pricing tiers as needed with no manual intervention|
|Cost savings||Utilize native Cluster Autoscaler, requires substantial DevOps resources to configure and manage, and is difficult to adjust when faced with seasonality, cloud inventory availability, quotas and pricing changes||CAST AI median savings are 40% over and above what cloud providers’ Cluster Autoscaler solutions provide, and measurably more than other third party solutions in the market.|
#2: Manage cloud budget effectively
Low cloud cost visibility is one of the top issues IT budget owners struggle with.
Decision-makers can now regain control and transparency thanks to CAST AI’s reports and metrics. The platform’s real-time reporting capabilities enable DevOps and CloudOps teams to gain insight into their current cloud spend and forecast.
CAST AI also leverages Prometheus scrape metrics to generate alerts for any spend fluctuations on an hourly and daily basis. As a result, you can display data in your favorite analytics tools like Grafana.
#3: Dynamically configure and plan for the highest cost savings
The survey of CAST AI customers proves that the platform provides better cost savings than reserved instances (RIs) and savings plans (SPs). While it supports RIs for base load, its wide range of cloud cost optimization helps users drop upfront commitments altogether.
For applications where clusters have a stable base load, CAST AI provides a way to leverage RIs to achieve even more significant cost savings. The platform accomplishes this by deploying low-risk and high-efficiency bin packing algorithms. They consolidate nodes when application demand declines, resulting in a substantial reduction of wasted cloud resources.
Moreover, the platform allows optimizing configuration in real time, eliminating accumulated resource fragmentation in nodes.
Lastly, the CAST AI platform features spot instance automation, selecting the best cost and performance combination spot VMs, while effectively handling out-of-capacity, out of quota or other limitations. With bin packing and automatic instance type selection, spot instances provide substantially higher savings than Reserved Instances – and don’t force you into a long-term commitment.
#4: Use best-in-class Kubernetes automation
CAST AI was built with Kubernetes automation in mind. The platform’s algorithms identify the best combination of high-performance CPUs, local NVMe drives and ultimately provide the best cost/benefit ratio for resource/cost calculations.
With over 500 instance types on AWS, and each zone having different availability and pricing, those variables change many times per day. It’s futile to plan and manage this manually, even with dedicated full-time DevOps resources.
This can result in potential failures and applications running out of capacity and more – and CAST AI seeks to eliminate these challenges with its comprehensive approach to Kubernetes automation.
#5: Outclass native cost-saving models
When working with CAST AI clients, we’ve also learned that native cost savings strategies fall increasingly short compared to the platform’s capabilities.
The native Cluster Autoscaler has several drawbacks. For instance, it requires substantial DevOps resources to configure and manage, and it is difficult to adjust when faced with seasonality, cloud inventory availability, quotas, and price changes.
One of the critical areas for effective cloud cost management is the ability to scale near real-time to changing resource demand. Rather than having DevOps teams manually create and manage node pools, limiting how well the Kubernetes Scheduler can perform, CAST AI relies on its just-in-time scaling algorithms.
As a result, the platform can benefit from the full power of the Kubernetes Scheduler, bringing measurable improvements in resource utilization and cost management over manual methods.
#6: Outclass third-party cost-saving models
Many CAST AI customers have previously used another autoscaling solution, either Kubernetes-native or a third-party one.
Without spot instance automation, which by default brings substantially higher savings, CAST AI median savings are 40% over what cloud providers’ cluster autoscalers deliver. They are also measurably higher than other third-party solutions in the market.
#7: Automate multi-cloud deployment and management
We see an increasing number of CAST AI customers using multiple cloud providers (e.g. EKS and AKS).
While this adds to the complexity for a DevOps team to manage requirements across multiple providers, CAST AI by design handles multi-cloud deployment and management automatically.
See Kubernetes automation in action
There are many ways in which automation can streamline your cloud costs and processes. Connect your cluster and run a free report to see how CAST AI can help improve your application environment for Kubernetes automation, cost optimization, monitoring and security.
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