“We were essentially able to reduce the cost of that cluster by about 75%. On AWS, DevZero demonstrated they could achieve significantly higher savings than we initially thought possible.”
Mihir Nair
Head of Architecture, Databahn
Kubernetes infrastructure operations
that actually scale
DevZero continuously analyzes your Kubernetes clusters and cuts infrastructure costs without touching application performance. Unlike Karpenter, it adjusts CPU and memory in place using MPA. No node replacement. No restarts.
Initial State
Applying Policy
Optimized
Before
After
c5.4xlargeOn-demand
$0.680/hr
c5.2xlargeOn-demand
$0.340/hr
c5.xlargeOn-demand
$0.170/hr
+3more nodes
6 nodes all
on-demand
on-demand
c5.4xlargeOn-demand
$0.680/hr
c5.2xlargeOn-demand
$0.340/hr
c5.xlargeOn-demand
$0.170/hr
+3more nodes
On-demand — all 6 nodes
avg utilization: 28% · 72% wasted28% utilized / 72% idle waste
All 6 nodes running on expensive on-demand instances
Companies who slashed their Kubernetes
spend using DevZero
Every resize stays inside your policy
DevZero adjusts CPU and memory based on observed workload behavior. Choose Conservative, Balanced, or Aggressive per namespace. Hard min/max boundaries define what recommendations can never cross. autoApply is off by default. Changes queue for approval until you enable it. Models are per workload, not cluster-wide averages.
Spend mapped to the workload consuming it
Cost attribution follows the same namespace filters and label selectors in your policies. The data matches exactly how your team scoped their rules. Drill from cluster to individual pod. Trend data surfaces cost drift before it becomes a problem.
One policy or fifty the fleet adapts
Target by namespace, label, kind, or name pattern. Precedence is clear when rules overlap. autoApply is set per policy; production waits for approval while dev runs automatically. Node group policies are separate. Instance families, spot vs on-demand, and consolidation aggressiveness are independent from workload rules.
Different workloads per workload, not per cluster
Write scoped policies per workload type. Tight boundaries and manual approval for production. Aggressive P90 rightsizing for dev and staging. A dedicated GPU policy targets nvidia accelerator labels directly. Node group policies handle spot preference and consolidation aggressiveness per pool, fully independent from workload rules.
Built for platform engineers
The controls your team actually needs, not a dashboard that requires a PhD to interpret.
cgroup v2 · live
CPU throttle
78%Mem PSI stall
43msOOM events
0sub-second
prometheus · 15s avg
CPU usage
38%Mem usage
51%Throttle visible
—averaged out
P99 latency spike · api-server-7f6d9
throttle_pct=0.78 · caught in 180ms window
Detected
DevZero reads CPU throttle rates, memory pressure (PSI), and OOM events directly from cgroup v2, not 15-second Prometheus averages. Sub-second anomalies that cause P99 latency spikes are visible and actionable.
EKS
841pods managed
GKE
519pods managed
AKS
302pods managed
On-prem
178pods managed
Single tenant1,840
pods · 4 clusters$41kMonthly Savings
3Cloud Providers
4Clusters
1Tenant
One DevZero tenant manages rightsizing across EKS, GKE, AKS, and on-prem clusters. Monthly savings and optimization metrics are aggregated in a single view across all cloud providers.
DEVZERO
cpu.request: 2000m → 680m · conf=0.94SLACK · #PLATFORM-RIGHTSIZING
Advisory mode · saves $2.84/hrGITHUB · HELM-VALUES PATCH
resources.requests.cpu: "680m"Advisory mode surfaces recommendations where your team already works. GitHub PR integration proposes changes before anything touches production. Slack integration posts to the on-call channel, keeping the right people informed.
What our customers say
Frequently asked questions
What are Kubernetes infrastructure operations?+−
Kubernetes infrastructure operations involve managing, optimizing, and maintaining Kubernetes clusters that run production workloads. This includes cluster monitoring, resource optimization, autoscaling, cost visibility, and workload management to ensure applications run reliably and efficiently. Modern platforms also automate resource tuning and infrastructure management to reduce operational overhead.
Why are Kubernetes infrastructure operations important for platform teams?+−
Platform teams rely on Kubernetes infrastructure operations to ensure clusters remain stable, cost-efficient, and scalable. Effective operations help teams manage cluster resources, prevent overprovisioning, optimize node utilization, and maintain high availability across environments.
What challenges do teams face when managing Kubernetes infrastructure?+−
Many teams struggle with Kubernetes infrastructure complexity, including overprovisioned resources, poor visibility into workload costs, inefficient node utilization, and unpredictable scaling behavior. These issues can lead to wasted cloud spend and operational inefficiencies if not addressed with proper monitoring and automation tools.
How can Kubernetes infrastructure operations reduce cloud costs?+−
Kubernetes infrastructure operations reduce cloud costs by improving resource efficiency. Techniques such as workload rightsizing, intelligent bin packing, node optimization, and autoscaling help ensure CPU, memory, and GPU resources match actual workload demand rather than static estimates.
What tools help automate Kubernetes infrastructure operations?+−
Tools designed for Kubernetes infrastructure operations provide automation for workload optimization, node management, scaling policies, and resource monitoring. Advanced platforms can continuously analyze cluster usage and automatically adjust resources in real time to eliminate waste and improve efficiency.
How do platform teams monitor Kubernetes infrastructure performance?+−
Platform teams monitor Kubernetes infrastructure using observability tools that track metrics such as CPU utilization, memory usage, node health, workload performance, and cluster capacity. Real-time visibility helps teams identify inefficiencies and optimize cluster resources before they impact performance or costs.
Stop paying for CPU and memory you're not usingStart optimizing your infrastructure today
Connect your cluster in under 30 minutes. No code changes. No pod restarts. First savings visible within 24 hours.