Workload Rightsizing
Analyze CPU and memory usage patterns to recommend optimal resource requests and limits. Eliminate over-provisioning without risking performance.
How it Works
Three steps from setup to savings
Collect
The agent continuously profiles CPU and memory usage per container over a configurable 14-day window. No code changes needed.
Analyze
Statistical analysis (P50-P99) with growth and spike buffers generates safe recommendations per workload.
Review
Review recommendations in the dashboard and export optimized YAML manifests for your team to apply.
What's Included
Intelligent Resource Optimization
Container-Level Resource Analysis
See exactly how much CPU and memory each container uses versus what it requests. Identify waste at the container level, not just the pod level.
- Per-container CPU and memory utilization with provisioned vs actual comparison
- Instant savings calculation showing exactly how much each container wastes per month
Smart Recommendations
Statistical recommendations that account for traffic patterns, growth trends, and seasonal variations. Each suggestion is backed by the data so you can make informed decisions.
- Algorithm accounts for peak usage, daily cycles, and weekly traffic patterns to generate safe recommendations
- Priority-ranked by savings potential so you know which workloads to optimize first
Before/After Resource Comparison
Side-by-side view of current vs recommended resource configurations. See exactly what will change before you apply, including requests, limits, and the projected cost impact.
- Full resource diff with CPU requests, limits, memory requests, and limits side by side
- Export recommended values as YAML to apply directly via kubectl or your GitOps pipeline
Safety Guardrails
Built-in safety analysis prevents recommendations that could cause OOM kills or CPU throttling. Every suggestion is validated against stability metrics and buffer requirements.
- OOM risk scoring with safe zones, warning zones, and danger thresholds clearly visualized
- Trend and volatility analysis ensures recommendations account for traffic spikes and seasonal patterns
Recommendation Lifecycle
Track each recommendation from detection through resolution with full status tracking and audit trail. Monitor which optimizations were adopted and savings realized.
- Monitor recommendation status across all workloads with real-time savings calculations
- Historical tracking shows which optimizations were adopted and cumulative cost reductions
Savings Tracking
Track the real impact of your rightsizing decisions. Compare before and after costs, monitor cumulative savings over time, and see which recommendations delivered the most value.
- Before/after cost comparison for every applied recommendation with percentage change
- Active/Saved tabs to separate pending opportunities from already-applied optimizations
Frequently Asked Questions
Common questions about Workload Rightsizing
See What 60% Savings Looks
Like on Your Clusters
Stop overpaying for Kubernetes. See potential savings within 10 minutes.