Kubernetes clusters run hot and empty at the same time. Engineering teams provision generously to avoid outages, then let that headroom sit unused month after month because nobody has the time to go back and right-size hundreds of workloads by hand. Cast AI builds its entire pitch around that gap between what teams pay for and what their clusters actually use and around fixing it automatically instead of just reporting on it.
What Kubernetes Waste Actually Looks Like
The scale of the problem is bigger than most teams assume. Cast AI’s 2026 State of Kubernetes Optimization Report, drawn from tens of thousands of clusters across AWS, Azure and GCP, found average CPU utilization sitting at just 8%, memory utilization at 20% and CPU overprovisioning running at 69%. That means the typical cluster is paying for roughly five times the compute it needs on any given day.
The same report found that organizations using automated rightsizing cut their provisioned CPU footprint by about 50% on average. That single number explains why so much of the Kubernetes tooling market has shifted from dashboards toward automation that actually acts on what it finds, a shift also tracked by the Cloud Native Computing Foundation’s surveys on production Kubernetes adoption.
What Cast AI Is
Cast AI is a Kubernetes optimization platform built around reliability and performance as much as cost. It uses SLO (service level objective) signals, the reliability and latency targets a team sets for its own applications, to take guardrailed actions in production rather than just flagging problems for an engineer to fix later.
That distinction matters. A lot of cost tools stop at visibility: they show you a dashboard of spend by namespace or node pool and leave the rightsizing, the scheduling changes and the instance swaps to whoever owns the cluster. Cast AI continuously analyzes workloads and automates rightsizing, autoscaling, bin packing, infrastructure provisioning, rebalancing and Spot instance usage instead of stopping at recommendations someone still has to implement. That is the core difference between an optimization platform and an optimization report.
How It Works in a Running Cluster
The mechanics are less about a one-time audit and more about a constant feedback loop:
1. The platform watches live workload behavior, not historical averages, so it catches usage patterns as they shift.
2. It rightsizes both workloads and the nodes running them, matching requested CPU and memory closer to what’s actually consumed.
3. It bin packs workloads onto fewer, better-fitted nodes and rebalances the cluster as demand changes throughout the day.
4. It provisions infrastructure automatically, including moving eligible workloads onto Spot instances, a cheaper but interruptible type of cloud compute, when it’s safe to do so.
5. SLO signals act as guardrails throughout, so aggressive cost moves don’t get made at the expense of an application’s reliability targets.
Where the Automation Shows Up in Practice
Case studies published by Cast AI give a sense of what that loop produces once it’s running against real production traffic. Moonshot Marketing reported roughly 40% lower AWS costs within the first month, with the savings sustained afterward through rightsizing, autoscaling, Spot and on-demand policy management, bin packing and rebalancing working together rather than as one-off fixes.
InCred Finance reported about 30% in monthly compute savings despite going in believing its environment was already well optimized, which is a useful data point for teams that assume they’ve already captured the easy wins. Altruist reports consistent savings of 45% or more, alongside recovering roughly 85 to 130 engineering hours per month that had previously gone into manual rightsizing and incident response.
Those recovered engineering hours are arguably as important as the dollar savings, since they represent time an infrastructure team gets back for building instead of babysitting cluster configuration. Cast AI has also been ranked #1 out of 223 solutions in its category, a data point worth noting alongside the case study figures.
Strengths
Automation over recommendation. Plenty of cost tools tell you a pod is overprovisioned. Cast AI’s rightsizing, bin packing and Spot instance management happen without an engineer manually applying every change, which is the main thing that separates it from allocation and reporting dashboards.
Reliability-aware decisions. Because the platform ties its automated actions to SLO signals rather than cost alone, it’s built to avoid the classic optimization trap where a cheaper cluster becomes a less reliable one.
Documented outcomes across different environments. The published results span a marketing company, a finance company and a wealth management platform, three different scale and workload profiles, all reporting meaningful compute savings.
Time recovered, not just money. Altruist’s reported 85 to 130 hours a month back is a concrete example of what “automated” actually buys a team beyond the cloud bill.
Honest Drawbacks
Automation of this depth is aimed squarely at teams already running production Kubernetes at real scale. A small team running a handful of low-traffic services may find the platform is more capability than they need and simpler cost-reporting tools could cover their situation without the setup involved in wiring in SLO-based guardrails.
Handing rightsizing, bin packing and node provisioning to an automated system also means trusting that system with decisions engineers used to make by hand. Teams with strict change-management processes will want to understand exactly how the guardrails work before turning automation loose on production.
The published savings figures, 40%, 30%, 45% and up, come from named customer case studies rather than a universal guarantee. They’re a reasonable signal of what’s possible, not a number every environment will automatically hit, since workload mix and existing overprovisioning vary a lot between organizations.
Who Cast AI Is Best For
Platform engineering, DevOps, site reliability engineering, cloud infrastructure and FinOps teams running production Kubernetes at real scale are the clearest fit. These are the teams that feel the 69% overprovisioning problem directly, in on-call fatigue as much as in cloud bills and that have enough workload volume for automated rightsizing to produce a measurable difference.
Teams that only need a monthly report of where cost is going may not need everything the platform does. But organizations looking for continuous, automated optimization rather than a static snapshot of last month’s waste are the ones this platform is built around.
The Verdict
The numbers in Cast AI’s own optimization report, 8% CPU utilization and 69% overprovisioning across tens of thousands of clusters, describe a problem that’s bigger and more structural than most teams admit to themselves. The customer results, from Moonshot Marketing’s early cost drop to Altruist’s ongoing 45%-plus savings and recovered engineering hours, suggest the automation behind that report actually closes the gap rather than just measuring it.
For a platform, DevOps or FinOps team already spending real time manually rightsizing and rebalancing Kubernetes workloads, Cast AI is worth a serious look. For a smaller shop running a lighter Kubernetes footprint, it’s fair to weigh whether that depth of automation matches the size of the problem you actually have.