Common GKE Cost Challenges We Solve
Most teams waste 40-60% of their GKE budget. Here's how we fix it.
Problem: Overpaying for n1-standard when e2 works
Solution: Machine type recommendations based on workload requirements
Problem: Missing Preemptible/Spot VM savings
Solution: Automated identification of Spot-eligible workloads
Problem: Autopilot vs Standard confusion
Solution: Cost comparison analysis for your specific workloads
Problem: No visibility into namespace costs
Solution: GCP billing integration with namespace-level breakdown
How GKE Cost Optimization Works
Deploy to GKE
Install our agent via Helm. Integrates with GCP billing exports and Cloud Monitoring for comprehensive cost analysis.
Analyze Workloads
AI evaluates pod resources, node utilization, and compares Standard vs Autopilot costs for your specific workloads.
Implement Savings
Apply right-sizing, migrate to Preemptible/Spot VMs, and optimize Committed Use coverage with actionable recommendations.
GKE-Specific Cost Optimization Features
Purpose-built features for Google Kubernetes Engine cost management.
Autopilot Mode Analysis
Compare Standard vs Autopilot costs for your workloads. Get recommendations on when Autopilot's per-pod pricing saves more than managing nodes.
Preemptible & Spot VM Adoption
Identify stateless workloads suitable for Preemptible or Spot VMs. Save up to 80% on compute with automated interruption handling.
Pod Resource Right-Sizing
Analyze actual CPU and memory usage patterns. Generate optimal resource requests and limits to eliminate waste.
GCP Billing Integration
Direct integration with GCP billing exports. Track costs by namespace, label, or GKE cluster with BigQuery analytics.
Committed Use Discount Analysis
Get recommendations for GCE Committed Use Discounts based on your GKE baseline usage. Maximize reserved pricing savings.
Node Auto-provisioning Optimization
Fine-tune NAP settings for cost-efficient node selection. Balance machine type diversity with cost optimization.
Deep GCP Integration
Native integration with Google Cloud services for complete GKE cost visibility.
GCP Billing Export
Direct BigQuery integration for accurate cost tracking
Cloud Monitoring
Pull metrics for comprehensive resource analysis
Node Auto-provisioning
Optimize NAP for cost-efficient node selection
Committed Use Discounts
Analyze and recommend optimal CUD coverage
Persistent Disk
Identify idle and overprovisioned storage
GKE Autopilot
Cost analysis and optimization for Autopilot mode
"We were using GKE Standard with n1-standard-8 nodes across 8 clusters. DeepCost showed us that switching 3 dev clusters to Autopilot and using Preemptible VMs for batch workloads would cut costs dramatically. We went from $28K to $11K monthly in just 6 weeks."