Kubex vs. Cast AI: The Cast AI replacement that works with HPA, VPA, CA, and Karpenter
Cast AI replaces native cluster-autoscaling technologies such as the Cluster Autoscaler, Karpenter, and HPA. In contrast, Kubex optimizes your clusters by leveraging proven autoscaling technologies.
Why teams turn to Kubex to replace Cast AI
- Use proven technologies like HPA, KEDA, Karpenter, and the Cluster Autoscaler
- Optimize pod requests, bin packing, cluster size, node types, and the timing of scalings
- Support CPUs with traditional workloads as well as GPUs with AI and ML workloads
- Implement governance via API by integration with GitOps pipelines or ticketing systems
If you use Cast AI and are considering an alternative, reach out for a demo and a free technical consultation session and speak with an experienced solution architect.
Kubex vs. Cast AI
Seven capabilities where the two platforms take different approaches.
Capability |
Kubex |
Cast AI |
|
| Cluster architecture | Runs on top of the autoscalers you already own. | Takes over node provisioning with proprietory technology for deeper automation. | |
| Optimization coverage | It tunes pods and nodes both vertically and horizontally within a single machine-learning-powered engine. | It primarily optimizes by provisioning and consolidating nodes, with four fixed runbooks that cover workload-level changes. | |
| How recommendations reach production | Support GitOps and webhooks in addition to tools like ServiceNow and Jira for governance. | Runbooks open GitHub pull requests. | |
| AI support | A conversational agent and an MCP server answer questions about cluster behavior. | Four runbooks that are fast and predictable for the scenarios they were written for. | |
| Handling stateful workloads | Pre-warms capacity ahead of predictable spikes such as batch windows and nightly processing to safely migrate production workloads. | Acts directly on nodes and moves production workloads, including stateful applications and databases. | |
| GPU and AI workloads | Compares and recommends GPU types, configurations, sizes, and regions. | Runs the GPUs you've already selected and optimizes for size and region. |
See what Kubex finds in your clusters
Start a free self-service trial. Run Kubex next to the autoscalers you already trust. Compare our recommendations against your current configuration, and decide for yourself.
Which platform fits your environment?
No tool is ever the right answer for every organization. The fit depends on your environment.
Choose Kubex if
- You want to use autoscalers like the Kubernetes Cluster Autoscaler, Karpenter, and KEDA
- You want one engine to factor the interplay between pod and node autoscaling
- You would like governance to include a human gate using tools like ServiceNow and Jira
- Your traffic follows known cycles, so capacity has to be ready before the spike arrives
- You need help selecting CPU and GPU types, not only size, region, and spot instances
Choose Cast AI if
- You want full automation of autoscaling and don’t require control
- You run GitOps-first with GitHub as the system of record
- Your estate is stateless-heavy, with live migration covering the stateful exceptions
- Your GPU hardware is already selected
Frequently asked questions
What teams ask most often when they evaluate replacing Cast AI.
What is the best alternative to Cast AI for Kubernetes optimization?
Kubex is the alternative for teams that want the savings without giving up control. It improves the autoscalers you own, including the Horizontal Pod Autoscaler, KEDA, Karpenter, and the Cluster Autoscaler.
What does Kubex actually optimize?
Continuously analyzes pod resource requests and limits, HPA targets, sizing for new containers with no history, bin packing across nodes, node type selection, and pre-scaling ahead of predictable load.
What happens if we turn Kubex off later?
You continue to run your cluster as before because implementing Kubex doesn’t require changes to your autoscaling technologies
Will Kubex conflict with our GitOps controllers?
No. Kubex is compatible with ArgoCD and Flux, and Git write-back is optional, so your controllers and Kubex do not overwrite each other’s resource values.
Can Kubex handle scenarios that a prebuilt runbook does not cover?
Yes. Cast AI’s Application Performance Automation ships four runbooks that are fast and predictable within their scope. Kubex adds a conversational AI agent and an MCP server, so platform engineers can ask about cluster optimization in plain language and get answers for cases that fall outside a fixed model, such as JVM-heavy applications, multi-tenant clusters with conflicting resource policies, and non-standard traffic patterns.
How do Kubex and Cast AI differ on GPU workloads?
Cast AI’s OMNI Compute finds the cheapest place to run a GPU workload you have already selected across 125 regions. Kubex’s Catalog Map scores GPU types and recommends the type each workload should use, including A100 MIG fractions, the inference-focused L4, the top-end B200, and the mid-range Azure A10.
Ready to replace Cast AI?
Start for free with our self-service trial, or walk through your requirements with our solution engineering team before you decide.
