Why Cast AI is Best for Kubernetes Automation

Managing Kubernetes clusters manually is complex, error-prone, and time-consuming. Cast AI flips the script by automating cluster management while giving teams control over how automation mechanisms are applied. From autoscaling and resource selection to workload rightsizing and bin-packing, Cast empowers DevOps teams to focus on innovation rather than managing cloud infrastructure manually. 

In this article, we’ll explore how Cast AI eliminates the grind of manual operations for Kubernetes clusters.

5 Kubernetes automation mechanisms Cast offers

1. Cluster autoscaling

Cast’s Autoscaler automatically adjusts cloud capacity to meet real-time demand fluctuations without causing any downtime. It’s simple to set up and will operate automatically according to the policies you pick for each cluster. These policies are called Node Templates; they define virtual buckets with limits on instance kinds, node lifespan, node provisioning, and other features.

Real-life example: Foretellix 

Foretellix, the leading provider of data automation for AI-powered autonomy, utilizes the Cast Autoscaler to efficiently manage the spiky demand patterns of short-lived activities, provisioning compute resources in under two minutes.

The image below illustrates how the cluster’s capacity rapidly increased from zero to 2000 CPUs, then decreased back to zero.

A similar rapid node scaling pattern, observed over a 30-day timeframe, was employed to enhance resource efficiency and optimize computational costs.

2. Bin-packing pods to nodes

The Kubernetes scheduler distributes pods across nodes in a fair manner, rather than maximizing node utilization or cost efficiency. To increase resource utilization and avoid paying for unused resources, teams must bin-pack pods to nodes and immediately destroy empty nodes. Because things move so quickly with Kubernetes, manually doing that is challenging.

Cast includes automated bin-packing, a mechanism that continuously compacts pods into fewer nodes, resulting in empty nodes that can be removed via the Node deletion policy (if enabled). To avoid downtime, Evictor will only examine apps that have multiple replicas. To save even more money on your cluster, Evictor selectively removes empty nodes that haven’t been used in a set amount of time; you’ll never pay for idle resources again.

Real-life example: Mercedes-Benz.io 

The Mercedes-Benz.io team runs a regular rebalancing operation using Cast. New VMs are provisioned and pods are bin-packed to maximize resource utilization and reduce costs.

3. Workload Autoscaler

Setting workload demands and limitations in Kubernetes is difficult because it requires a thorough understanding of both the application’s resource usage habits and the cluster’s capabilities.

Misconfigured values might result in inefficient resource utilization. Too low, and workloads may be throttled or evicted; too high, and they may starve other pods or drive up infrastructure expenses. Striking the balance is difficult due to the dynamic nature, unforeseen traffic spikes, and the lack of exact profiling tools.

Cast AI’s Workload Autoscaler continuously analyzes actual CPU and memory usage patterns and dynamically adjusts resource allocations, requiring no operator intervention or downtime. This includes in-place pod resizing, which allows Cast AI to change resource parameters for running pods immediately.

Real-life example: Bud 

Bud, a company that provides actionable data for LLMs in the financial services sector, utilizes Workload Autoscaler to accurately size workloads, reduce CPU requirements, and unlock additional cost savings.

The figure below depicts the influence of Workload Autoscaler on compute cluster costs.

This graph depicts the decline in requested CPUs per hour once Cast was integrated.

4. In-place pod resizing automation

Before in-place pod resizing, tuning resource requests (CPU or RAM) for a running pod required shutting it down and spinning it back up, effectively restarting the workload. This presented various challenges:

• Downtime or performance issues during pod restarts.
• Manual intervention or complicated restart automation logic
• Inability to react swiftly to short-term consumption spikes or drops
• Missed optimization potential for autoscalers and cost-saving systems

As a result, teams frequently overprovision to prevent risk, resulting in wasted resources and increased cloud costs. Even with Vertical Pod Autoscaling (VPA), the lack of in-place updates results in constant friction between optimization and operational safety.

In-place pod resizing is a key feature for workloads that experience brief spikes in demand or drop off following peak periods.

While Kubernetes now offers this functionality at the platform level, it doesn’t determine when to make changes or how much to alter resources. Teams must still monitor metrics in real-time to determine if adjustments are required, and then apply resource updates via API requests or manifests.

Cast eliminates the need for manual monitoring and decision-making by automatically detecting resource inefficiencies and making the necessary adjustments at the right moment. The Cast Workload Autoscaler engine now fully supports in-place pod resizing, managing both the decision-making and execution processes.

Here’s how it works:

• Cast continuously analyzes resource use across all workloads in the cluster
• When it finds over- or under-provisioned pods, it computes the optimal CPU and memory requirements
• If the pod allows in-place resizing, Cast automatically applies the modifications without restarting the pod
• If a restart is necessary (for example, when transitioning between limitations or when a container does not enable in-place changes), Cast AI handles it safely and smartly to minimize damage

There’s no need for manual resizing or guesswork – no YAML modifications, kubectl commands, or alerts to chase.

5. Container Live Migration

Organizations running resource-intensive, stateful applications need to maintain high availability and cannot afford downtime. However, due to a lack of widely adopted commercial solutions for migrating these workloads – such as moving stateful apps to more cost-efficient infrastructure, transitioning legacy systems to Kubernetes, or adapting non-Spot-ready workloads for Spot Instances – they are often forced to run on underutilized, expensive nodes.

Here’s where Cast’s Container Live Migration comes in. The solution ensures that stateful applications remain resilient and responsive by allowing for seamless transitions from one node to another without affecting application processes.

These formerly intractable workloads are now automatically combined onto fewer nodes, resulting in improved continuous availability, reduced resource fragmentation, and substantial cost savings.

Furthermore, Cast’s Evictor maximizes cluster utilization by moving workloads from underutilized nodes, delivering significant cost savings without service disruption. Traditional bin-packing approaches were constrained by stateful workloads; live migration overcomes these constraints.

The solution enables teams to maintain active TCP connections and session state during migration, thereby reducing disruptions to client applications and ongoing transactions. Applications with strict timeout requirements may require appropriate timeout settings to manage the limited migration window.

Ultimately, with Container Live Migration, users can improve bin-packing efficiency while significantly lowering node fragmentation and cloud infrastructure costs. This increases resource efficiency and lowers costs by maximizing the effectiveness of both the Evictor and Rebalancing capabilities.

Conclusion

In a world where complexity grows by the minute, Cast’s automation features are key to simplifying Kubernetes cluster management. By eliminating manual tasks, optimizing costs in real-time, and scaling workloads efficiently, Cast empowers teams to move faster and maintain high resource utilization.

If your goal is to unlock the full potential of Kubernetes without the operational drag, Cast is the upgrade your infrastructure deserves.