Eliminate 60-80% of Resource Waste
Stop overprovisioning Kubernetes resources. Use real workload data to right-size CPU and memory requests, eliminating waste while maintaining reliability.
The Overprovisioning Problem
Kubernetes teams typically overprovision by 3-5x actual usage, leading to massive resource waste and unnecessary cloud costs.
Conservative Resource Requests
Teams set high CPU and memory requests "to be safe," often requesting 3-5x more resources than applications actually use in production.
No Usage Visibility
Lack of real-time usage data means teams can't right-size resources, perpetuating overprovisioning patterns across the entire cluster.
Fear of Performance Issues
Teams prioritize avoiding performance problems over cost efficiency, leading to systematic overprovisioning as the default approach.
Overprovisioning Impact
*Industry benchmarks from Kubernetes cost optimization studies
Data-Driven Right-Sizing Strategy
Use real workload data to eliminate overprovisioning while maintaining performance and reliability through intelligent resource optimization.
Real Usage Analysis
Continuous monitoring of actual CPU and memory usage patterns across all pods, namespaces, and workloads in your clusters.
- • 7-day minimum data collection
- • Peak and percentile analysis
- • Workload pattern recognition
Intelligent Recommendations
AI-powered analysis generates safe right-sizing recommendations with built-in safety margins and performance considerations.
- • P95 percentile + safety buffer
- • Workload-specific optimization
- • Gradual rollout strategies
Automated Implementation
Automated resource updates with rollback capabilities, performance monitoring, and impact validation.
- • Canary deployments
- • Automatic rollback on issues
- • Continuous optimization
Advanced Right-Sizing Technology
Sophisticated algorithms that understand workload patterns, seasonal variations, and performance requirements.
Percentile-Based Sizing
Use P95 percentile usage data with configurable safety margins to ensure performance while eliminating waste.
Workload Classification
Automatically classify workloads (web servers, databases, batch jobs) for workload-specific optimization strategies.
Seasonal Pattern Detection
Identify daily, weekly, and monthly usage patterns to right-size for actual demand cycles.
Performance Impact Monitoring
Continuous monitoring of application performance metrics to validate optimization decisions.
Right-Sizing Results
Results from production Kubernetes clusters after right-sizing implementation
Safe Right-Sizing Process
Data Collection
7-day minimum monitoring of actual resource usage patterns across all workloads.
Analysis & Recommendations
AI-powered analysis generates safe optimization recommendations with performance validation.
Gradual Rollout
Implement changes gradually with canary deployments and automatic rollback capabilities.
Continuous Optimization
Ongoing monitoring and adjustment based on changing workload patterns and requirements.
Real-World Right-Sizing Results
E-commerce Platform
200-node cluster reduced from 25% to 75% utilization, saving $180K annually while improving performance.
SaaS Company
Microservices architecture right-sized from 1000 overprovisioned pods to optimal resource allocation.
Financial Services
Critical workloads optimized with safety-first approach, achieving compliance and cost efficiency.
Stop Wasting 60-80% of Your Kubernetes Resources
Start right-sizing your workloads based on real usage data, not guesswork.