GPU Scarcity is Real.
Waste is Optional.
Stop paying for idle GPUs. Control Kubernetes GPU allocation for AI workloads.
Idle GPU Elimination
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.
Workload Optimization
Workload-Level GPU Optimization
Traditional GPU management operates at node level, missing significant waste. DevZero provides workload-level optimization by monitoring individual GPU allocations and releasing them when specific jobs complete or go idle, not just when entire nodes are empty.
Workload-Level Detection
Node-level autoscalers scale down empty nodes. But a node with one small workload holding a full GPU will not scale down. DevZero releases that GPU allocation while the node remains active. This captures waste across all AI workload patterns: batch model training, AI inference serving, and exploratory ML work.
Seamless Integration
DevZero complements tools like Karpenter and KEDA without replacing them. While those handle node capacity, DevZero optimizes GPU allocations per workload.
Maximum Utilization
Do More with Existing GPUs
Most teams do not need more GPUs. They need better utilization of existing capacity. At typical 20-30% utilization, organizations pay for 100%. For teams constrained by GPU availability or budget, optimization means more work gets done without expanding infrastructure.
Cost and Capacity Optimization
DevZero delivers two critical outcomes: control AI costs by eliminating waste from idle GPUs across model training, inference serving, and exploratory workloads; and do more with existing capacity so GPUs handle more AI workloads without additional hardware.
Dynamic Allocation
GPUs are treated as dynamic resources, not static infrastructure. Allocated when needed, released when idle, managed continuously by policy. No manual cleanup required. Just GPU spend aligned to actual workload behavior.
Customer Results
Slashing GPU cluster cost by $776K alongside Karpenter.
Who: An enterprise AI/SaaS company delivering real-time event detection and alerting for enterprises and first responders.
Need: Optimize Kubernetes and GPU costs, gain clearer cost visibility by department or namespace, and implement safe, low-touch automation.
Slashing workload cost by 80% in 12 hours.
Who: A platform helping enterprises build and deploy AI models in their own cloud (BYOC), offering a managed Metaflow-based platform.
Need: Cut Kubernetes costs by reducing overprovisioning, node fragmentation, and churn while maintaining performance.
Slashing compute by 50% in 24 hours. Cutting cost by 80% in 5 days.
Who: A cybersecurity data platform whose Security Data Fabric streamlines and federates data ingestion.
Need: Reduce high AWS/Azure cloud spend caused by under-utilized and fragmented nodes without impacting customers.
Get started in minutes
Install a read-only operator
Deploy with a single command on Amazon EKS, Google GKE, Azure AKS, Oracle OKE, or any self-hosted Kubernetes cluster.
Gather metrics and calculate waste
See workload cost, CPU, and memory utilization with detailed breakdowns across your clusters, namespaces, and workloads.
Define policies and optimize
Set optimization policies per cluster, node pool, or workload with advanced controls for CPU, memory, GPU, and live migration.
Eliminate GPU Waste with Intelligent Automation
DevZero eliminates GPU waste through automated idle detection and policy-driven lifecycle management. No app changes. Just better utilization and controlled costs.