Kubernetes Cost Optimization
Optimize the resources and cost at the cluster, node, and workload level.
Live Rightsizing
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.
Predictive Scaling
Cost-Based Autoscaler
DevZero integrates with HPA, VPA, and Karpenter — it does not replace them. Instead, it adds a predictive layer that makes smarter, cost-aware decisions.
Beyond Reactive Scaling
Most autoscalers react to past usage, but DevZero predicts future demand. It handles bursty workloads such as CI pipelines, LLM inference, and memory-fluctuating JVM apps by analyzing CPU, memory, request patterns, and cost.
Full Control
You set policies. DevZero executes them intelligently. The system learns your workload patterns and gets more accurate over time. You maintain visibility and control while eliminating manual intervention.
Bin-packing
Node Optimization and Bin-packing
Kubernetes distributes pods fairly, not efficiently. Nodes run at 30-40% capacity while you pay for 100%. DevZero fixes this with intelligent bin packing and true zero-downtime migration.
CRIU-Based Live Migration
Other platforms restart workloads during migration. DevZero uses CRIU to snapshot and instantly resume them. What is preserved: memory and process state, TCP connections, filesystem, and session state. Migrate anytime — no downtime, cold starts, or drops.
Automated Consolidation
DevZero compacts pods onto fewer nodes, removing idle ones for max density and zero waste.
Instance Selection
Intelligent Instance Selection
Choosing the right instance type is complex. Compute-optimized? Memory-optimized? Spot or on-demand? Multiply this across regions, AZs, and workload types — manual management is impossible.
Real-Time Optimization
DevZero selects the most cost-efficient instance in real time. The algorithm considers: current pricing across regions and AZs, spot availability and interruption patterns, RI/Savings Plan utilization, and workload-specific requirements.
Dynamic Migration
As workloads evolve, DevZero uses CRIU to migrate with zero downtime — batch jobs to spot instances, memory-heavy apps to optimized nodes. Works with Karpenter to anticipate demand and optimize cost and performance.
GPU
GPU Optimization
GPU resources are costly and often underutilized. Teams overprovision for peaks; actual usage is 20-30%, costs soar.
Workload-Level Optimization
DevZero provides true workload-level GPU optimization — not just node-level scaling. The platform monitors actual GPU utilization and dynamically adjusts allocations based on real-time and predicted demand.
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.
Customer Results
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.
Slashing workload cost by 80% in 12 hours.
Who: A platform to help enterprises build and deploy AI models in their own cloud (BYOC), offering a managed Metaflow-based platform.
Need: Cut Kubernetes costs in their BYOC model by reducing overprovisioning, node fragmentation, and churn while maintaining performance.
Slashing GPU cluster cost by $776K alongside Karpenter.
Who: An enterprise AI/SaaS company that delivers 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.
Cut Kubernetes Costs with Smarter Resource Optimization
DevZero boosts Kubernetes efficiency with live rightsizing, auto instance selection, and adaptive scaling. No app changes — just better bin packing, higher node use, and real savings.