Your Kubernetes Cluster is Running at 8% CPU Utilization.
We Fix That.

Most companies pay for 20x more GPU than they use. We help engineering teams close the gap — turning idle infrastructure into competitive advantage.

Get a Free Cluster AuditView Services
5–8%
Average CPU utilization across Kubernetes clusters
$30/hr
Cost of an idle H100 GPU that's not being utilized
50–80%
Potential savings with Spot strategy & rightsizing
Services

Six Practices. One Goal: Zero Waste.

Structured around the exact problems draining your Kubernetes budget, framed as measurable outcomes — not vague promises.

Rightsizing Audit

"Reduce provisioned CPU by ~50% without touching application code"

We analyze your workload resource requests and limits against actual consumption patterns, identifying overprovisioned deployments and delivering actionable resize recommendations.

Autoscaler Assessment

"Replace reactive scaling with demand-aware provisioning"

Native autoscalers deepen the overprovisioning gap. We configure and tune HPA, VPA, and Karpenter to scale based on real demand patterns — not just CPU thresholds.

Spot Strategy & Automation

"Capture 50–80% savings on compute without sacrificing reliability"

We architect Spot-friendly workloads with intelligent fallback strategies, interruption handling, and multi-AZ scheduling to maximize savings while maintaining availability.

Premium

GPU Optimization

"Time-slicing, bin-packing, and scheduling for AI/ML workloads"

An idle H100 costs ~$30/hour. We implement GPU sharing, multi-instance scheduling, and workload bin-packing so your AI/ML teams get the compute they need at a fraction of the cost.

Node Lifecycle Automation

"Automated upgrades with audit trails for regulated industries"

Automated node rotation, OS patching, and version upgrades with full compliance audit trails — critical for fintech, healthcare, and other regulated environments.

Commitment Optimization

"98% Reserved Instance utilization without manual capacity planning"

We analyze your workload patterns and design commitment portfolios (Reserved Instances, Savings Plans, CUDs) that maximize discounts while preserving flexibility.

Discuss your specific cluster challenges
Premium Offering

GPU Waste is Where CFOs Feel Pain and Engineering Teams Have the Least Expertise

Companies deploying AI workloads on Kubernetes are bleeding money. They're spinning up GPU instances without MLOps background — paying on-demand prices for compute that sits idle 80% of the time. We bridge the gap between infrastructure and ML operations.

Multi-Instance GPU (MIG) partitioning for workload isolation
Time-slicing across training and inference workloads
Intelligent bin-packing to maximize GPU memory utilization
Spot GPU scheduling with preemption-aware checkpointing
Cost attribution and chargeback per ML team or experiment
Karpenter integration for GPU-aware node provisioning
Audit Your GPU Spend
$30/hr
Cost of an idle H100
That's $21,600/month per unused GPU
20x
GPU overprovisioning
Most companies pay for 20x more than they use
70%+
Potential savings
With time-slicing and bin-packing strategies

The Bottom Line

"We turn idle infrastructure into competitive advantage."

Who We Help

Built for Teams Who've Outgrown "Good Enough"

The waste isn't a technical oversight — it's a strategy gap. You need someone who understands the full stack: workload behavior, autoscaling, rightsizing, Spot scheduling, and GPU sharing.

Growth-Stage Startups

Series B–D on EKS / GKE / AKS

You've outgrown manual cluster management but haven't built a dedicated platform engineering team. Your Kubernetes bills are climbing while utilization stays flat.

No dedicated platform engineering team
Kubernetes costs growing faster than revenue
Engineering time wasted on infrastructure instead of product

AI/ML Companies

Deploying GPU workloads without MLOps background

You're spinning up GPU instances for training and inference but paying SageMaker or on-demand prices. Every idle H100 is $30/hour walking out the door.

Paying on-demand GPU prices with low utilization
No GPU sharing or scheduling strategy
Training jobs queuing while GPUs sit idle

Regulated Industries

Fintech, Healthcare & Compliance-Heavy

Node lifecycle management isn't just about cost — it's a compliance requirement. You need automated upgrades with audit trails that satisfy your auditors.

Compliance requires full audit trails for node changes
Manual patch management slows release cycles
Security scanning gaps in container lifecycle
Results

Measurable Outcomes, Not Vague Promises

Every engagement starts with a benchmark against industry averages — the 8% CPU, 20% GPU, and 5% memory utilization figures. Then we close the gap.

~50%

CPU Reduction

Average provisioned CPU cut through rightsizing — without touching application code

50–80%

Compute Savings

Captured through Spot strategy and intelligent fallback scheduling

98%

RI Utilization

Reserved Instance and Savings Plan coverage without manual capacity planning

< 2 weeks

Time to First Savings

From audit to implemented changes showing measurable cost reduction

Free Lead Magnet

Free Kubernetes Efficiency Audit

Every engineering leader who reads "8% CPU utilization is the industry average" immediately wonders where their cluster sits. Let's find out together.

A short, focused engagement that surfaces the waste percentage in your cluster and benchmarks it against industry figures. No commitment required — just data.

Cluster resource utilization benchmarking against industry averages
Workload rightsizing analysis with specific resize recommendations
Autoscaler configuration review (HPA, VPA, Karpenter/CA)
Spot Instance adoption opportunity assessment
GPU utilization analysis (if applicable)
Commitment coverage gap identification
Prioritized action plan with estimated savings per item

Get Your Free Audit

Tell us about your cluster and we'll schedule a 30-minute diagnostic call.

No commitment required · Results within 48 hours · 100% confidential