NewCompare CPU & GPU pricing across AWS, Azure & GCP

Kubernetes Cost optimization

Optimize the resources and cost at the cluster, node, and workload level.

Total CPU Capacity

1,245.69 cores

Total Memory Capacity

4,816.72 GiBs

Total GPU Capacity

22 devices

$1000$500$200$100

Current Cost: $71.12

Usage Cost: $4.96

Requests

1,002.78 vCPU

Actually Used

58.16 vCPU

95.33%

Underutilization

Requests

3,356.95 GiB

Actually Used

751.78 GiB

84.39%

Underutilization

Requests

22.15 devices

Actually Used

0.49 device

97.79%

Underutilization

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Your cloud bill, finally under control.

DevZero continuously analyzes real-time GPU allocation and usage across your Kubernetes clusters, automatically identifying idle capacity, enforcing policy-driven controls, and reclaiming unused resources. By optimizing at the workload level and integrating with existing autoscalers, it ensures GPUs are efficiently utilized without disrupting active training or inference jobs.

Intelligent workload rightsizing

Traditional Kubernetes requires manual resource requests and limits. You overprovision for peak loads, then pay for idle capacity 80% of the time. DevZero fixes this with live rightsizing no pod restarts, no downtime.

How it works

DevZero uses XGBoost forecasting to predict future resource needs, avoiding inflated baselines for workloads that spike at startup. Optimization modes can be set per cluster, node pool, or workload: Statistical (steady, low-churn adjustments) or Predictive (ML-driven aggressive cost reduction).

Built-in safety

The platform monitors OOM errors, pod failures, and memory pressure, ensuring stability. Resources scale up during spikes and down when idle instantly.

Live Rightsizing

● Active
api-gateway
−59% cost
CPU16c→ 11c
Mem64G→ 22G
ml-serving
−78% cost
CPU32c→ 10c
Mem128G→ 32G
event-worker
−52% cost
CPU8c→ 7c
Mem32G→ 18G

Projected annual savings

$187,400

No restarts · No downtime

Ready to get started?

Try it yourselfBook a demo

How it works

DevZero continuously analyzes real-time GPU allocation and usage across your Kubernetes clusters, automatically identifying idle capacity, enforcing policy-driven controls, and reclaiming unused resources. By optimizing at the workload level and integrating with existing autoscalers, it ensures GPUs are efficiently utilized without disrupting active training or inference jobs.

3 simple steps

Install a read-only operator

Select your cloud provider:

Curl

$ curl -XPOST -H 'Authorization: Bearer ....' \
-H "X-Kube-Context-Name: $(kubectl config current-context)" \
"https://dakr.devzero.io/dakr/installer-manifest?cluster-provider=AWS" \
| kubectl apply -f -

Frequently asked questions

What our customers say

Databahn logo

“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

Mihir Nair

Head of Architecture, Databahn