“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
GPU scarcity is real. Waste is optional.
Optimize the resources and cost at the cluster, node, and workload level.
GPU optimization
DevZero continuously analyzes real-time GPU allocation and usage across your Kubernetes clusters, automatically identifying idle capacity and enforcing policy-driven controls — without disrupting active training or inference jobs.
Stop paying for idle GPUs
GPUs are expensive, scarce, and frequently over-provisioned for AI and ML workloads. Teams conservatively allocate resources, leaving capacity unused between jobs or during traffic lulls. The result? GPU spend driven by fear and guesswork, not utilization.
How it works
DevZero continuously monitors GPU allocation and actual usage across Kubernetes clusters. The system identifies three key waste patterns: ML training jobs that complete and leave GPUs idle, AI inference endpoints with warm pools consuming capacity during low traffic, and interactive notebooks left running after work ends.
Policy-driven management
You set the rules, DevZero executes them. Define allocation duration, cleanup triggers, and which workloads can access GPU resources at the cluster, namespace, or workload level.
Ready to get started?
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