Autonomous Kubernetes workload optimization
Run cost-effective workloads on peak performance with Cast Al’s intelligent workload optimization.
Instantly rightsizes workloads with zero downtime — thanks to in-place pod rightsizing and Live Migration(™) that preserves uptime even for stateful workloads. Includes live cost visibility, and autonomous stability enforcements.
2-minute install. Designed for Karpenter and the native autoscalers on EKS, GKE, AKS, OKE, all major clouds, on-prem, and Cast AI smart cluster autoscaler.
Trusted by 2100+ companies globally
Key features
Autonomously optimize Kubernetes workloads
Automated workload rightsizing
Continuously adjusts CPU and memory requests based on actual usage patterns.
- Analyzes historical and real-time metrics to apply optimal resource settings
- Prevents overprovisioning and underutilization without manual intervention
Seamless integration with HPA and VPA
Combines vertical and horizontal scaling for smart, adaptive resource management.
- Enhances Kubernetes-native autoscaling by layering in smarter, data-driven decisions
- Supports both short-term demand spikes and long-term workload trends
Zero-downtime container live migration
Move running workloads between nodes without interruption for stateful apps and long-running jobs while performing maintenance and optimizing costs.
- Enables migration of previously non-movable workloads backed by persistent storage
- Unlocks advanced bin-packing by eliminating node fragmentation and keeps critical applications running
Automatic in-place pod resizing
Dynamically modifies resource allocations of running pods without restarts, ensuring zero downtime.
- Adjusts pod limits and requests based on live demand
- Instantly react to shifting workload needs without service disruption, improving SLA adherence and user experience
Extensive workload support
Optimizes a wide range of Kubernetes workloads with flexible configuration options.
- Supports Deployments, StatefulSets, Jobs, CronJobs, and custom workloads via label-based selection
- Apply consistent optimization across diverse workloads without changing deployment patterns
OOM event handling
Proactively prevents OOM crashes with adaptive memory tuning, improving workload stability.
Deferred scaling mode
Allows scaling changes to be queued and applied during maintenance windows or low-traffic periods.
Automatic surge response
Instantly scales resources in response to unexpected traffic spikes to maintain performance.
Setup
Get started in three steps
Select your provider and run a single script to deploy a lightweight, read-only agent that will analyze your cluster.
Set your policies and let Cast AI optimize your cluster automatically.
Keep your cluster optimized with automation at every step.
Case study
Bud achieved 90%+ resource utilization, reduced costs, and increased engineer productivity
“Enabling the Workload Autoscaler brought instant savings. Before Cast AI, I spent about six months trying to educate people on how to properly set their CPU, memory, and scaling configurations. With the Workload Autoscaler, all of that happens automatically, so the development teams can focus on coding instead of tweaking configurations.”
Dan Udell
Director of Foundations Engineering
Learn more
Additional resources
Blog
How In-Place Pod Resizing Works in Kubernetes and Why Cast AI Makes It Better
Kubernetes 1.33+ introduces in-place pod resizing, allowing teams to change pod CPU and memory without restarts. See how Cast AI automates it.
Case study
Wio Bank saves up to 70% on compute resources using automation
With Cast AI, Wio Bank was able to increase profitability by improving the efficiency of its cloud infrastructure and reducing costs while maintaining performance.
Blog
How To Migrate Stateful Workloads On Kubernetes With Zero Downtime
How do you migrate stateful workloads on Kubernetes without causing downtime? This is where Cast AI Live Migration comes in.
FAQ
Your questions, answered
Workload optimization automatically scales CPU, memory, and replicas in real-time to maximize performance and reduce cloud spend.
It supports immediate and deferred modes and includes zero-downtime updates for single-replica workloads, ensuring safe resource changes without service interruption.
Yes. The Workload Autoscaler combines horizontal (HPA) and vertical (VPA) scaling, resolving conflicts to ensure efficient scaling that adapts to both spikes and long-term trends.
Cast AI uses live container migration, in-place pod resizing, and deferred scaling to apply changes without restarts or downtime, even for stateful and long-running jobs.
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