Your cloud bill is lying to you
You're paying for resources you're not using, discounts you're not claiming, and inefficiencies you can't see. We find the waste and eliminate it — without touching your architecture or performance.
Talk to an engineerWhy Choose THNKBIG for Kubernetes Cost Optimization
THNKBIG is a US-based Kubernetes FinOps consulting firm serving enterprises across Texas, California, and nationwide. We specialize in cloud cost optimization for organizations running production workloads on AWS, Azure, and GCP, combining deep Kubernetes expertise with financial accountability.
Our cost optimization consulting goes beyond simple right-sizing recommendations. We implement comprehensive FinOps practices including real-time cost attribution by team and workload, reserved capacity optimization, spot instance strategies, and the governance policies that prevent cost drift. Every optimization is validated against performance baselines.
Clients typically see 40-60% cloud cost reduction within the first 90 days, with quick wins often visible in the first two weeks. We establish sustainable FinOps practices that keep costs optimized long after our engagement ends, delivering ROI that compounds month after month.
Estimate Your Savings
Based on typical optimization results across our client base.
| Current Spend | Conservative (25-35%) | Typical (40-50%) | Aggressive (55-65%) |
|---|---|---|---|
| $100K/month | $25-35K/month | $40-50K/month | $55-65K/month |
| $500K/month | $125-175K/month | $200-250K/month | $275-325K/month |
| $1M/month | $250-350K/month | $400-500K/month | $550-650K/month |
Six levers for cost reduction
Every environment is different, but these are the optimization categories we consistently find across clients.
Compute Right-Sizing
20-35%Most workloads are over-provisioned. We analyze actual usage and right-size requests/limits.
Quick Wins
- Pod resource limits
- Node bin-packing
- Autoscaling tuning
Reserved & Spot Instances
30-50%Committed use discounts and spot instances for fault-tolerant workloads. Most orgs leave this money on the table.
Quick Wins
- Reserved capacity analysis
- Spot instance strategy
- Savings plans optimization
GPU Utilization
30-50%Average GPU utilization is 40%. We implement scheduling, time-boxing, and bin-packing to maximize expensive GPU resources.
Quick Wins
- GPU scheduling policies
- Time-boxing training jobs
- Fractional GPU allocation
Storage Optimization
15-30%Orphaned volumes, over-provisioned storage, wrong storage classes. Storage costs add up quietly.
Quick Wins
- Orphan volume cleanup
- Storage class optimization
- Snapshot policies
Network Costs
10-25%Cross-AZ traffic, NAT gateway costs, and egress fees. Architecture decisions have cost implications.
Quick Wins
- Topology-aware routing
- NAT gateway optimization
- Egress analysis
Idle Resources
15-40%Dev/test environments running 24/7, unused load balancers, zombie deployments. We find and eliminate waste.
Quick Wins
- Scheduled scaling
- Unused resource detection
- Environment cleanup
From visibility to governance
Visibility
Week 1-2
You can't optimize what you can't see. We instrument cost visibility across your clusters — by namespace, team, workload, and environment.
- Cost attribution setup
- Dashboard deployment
- Baseline measurement
- Anomaly detection
Analysis
Week 2-3
We analyze your cost structure against benchmarks and identify optimization opportunities. You get a prioritized roadmap.
- Resource utilization audit
- Savings opportunity map
- Priority ranking
- ROI projections
Optimization
Week 3-6
We implement optimizations in priority order: quick wins first, then structural improvements. Each change is validated before rollout.
- Right-sizing implementation
- Autoscaling configuration
- Spot/reserved strategy
- Storage optimization
Governance
Ongoing
Cost optimization isn't a one-time project. We implement policies, alerts, and processes to prevent cost drift.
- Cost policies
- Budget alerts
- Showback/chargeback
- Regular reviews
AI company cuts GPU costs by $340K per month
Series C AI Platform
The Challenge
A Series C AI company was spending $1.2M/month on GPU infrastructure with only 40% average utilization. No visibility into which teams or workloads were driving costs. Training jobs ran unattended for days.
Our Approach
- Deployed Kubecost for real-time cost visibility by team and workload
- Implemented GPU scheduling policies to maximize bin-packing
- Configured spot instances for fault-tolerant training jobs
- Set up time-boxing and automatic checkpointing for long-running jobs
- Created cost policies and budget alerts per team
Results
$340K
Monthly savings
94%
GPU utilization (was 40%)
100%
Cost visibility
2 weeks
To first savings
Why Kubernetes FinOps is essential for sustainable growth
The Cloud Cost Crisis Facing US Enterprises
Organizations across Texas, California, and throughout the United States are experiencing an uncomfortable reality: their cloud bills are growing faster than their revenue. The average enterprise wastes 30-40% of their cloud spend on idle resources, over-provisioned instances, and unclaimed discounts. Without proper Kubernetes FinOps practices, this waste compounds month after month.
Cloud cost optimization is not about cutting corners or degrading performance. It is about aligning your infrastructure spend with actual workload requirements. Every dollar saved on wasted compute is a dollar that can fund new features, expand your team, or improve your bottom line. For growth-stage companies and established enterprises alike, sustainable cloud economics determine long-term competitiveness.
Visibility Creates Accountability
The fundamental challenge with cloud costs is that they are invisible at the team level. Developers deploy workloads without understanding the cost implications. Platform teams lack the tooling to attribute spend to specific services, teams, or business units. Finance receives a monthly bill with no actionable breakdown. This opacity is where waste thrives.
Our Kubernetes FinOps approach starts with visibility. We implement cost attribution down to the namespace, deployment, and label level. Teams see their own spend in real time. Engineering decisions become cost-aware without slowing velocity. For organizations running on AWS, Azure, or GCP, this visibility transforms cloud infrastructure from a black box into a manageable, optimizable system.
The strategic imperative: In today's economic environment, cloud cost optimization is not optional. Boards and investors expect efficient capital deployment. Engineering leaders need to demonstrate ROI on infrastructure investments. Whether you are a Series A startup in Austin watching every dollar or a Fortune 500 enterprise in San Francisco managing millions in monthly cloud spend, disciplined FinOps practices separate sustainable businesses from those burning cash on invisible waste. Our clients consistently recoup their optimization investment within 60-90 days through measurable, documented savings.
Frequently asked questions
Technology Partners
Cost optimization in practice
60% latency cut and $85K/mo savings for a Fortune 500 energy company
We consolidated 47 Kubernetes clusters, right-sized workloads, and implemented autoscaling — slashing infrastructure costs while improving application performance.
Read the full case study →Related Reading
Kubernetes FinOps: Stop Burning Money on Clusters
Right-size resources, implement cost attribution, and cut cloud spend by 30-40%.
THNKBIG Partners with Kubecost for Cost Visibility
How Kubecost gives teams real-time cost attribution and optimization insights.
Running GPU Workloads on Kubernetes
Optimize GPU utilization with proper scheduling, time-slicing, and multi-tenancy.
Ready to make AI operational?
Whether you're planning GPU infrastructure, stabilizing Kubernetes, or moving AI workloads into production — we'll assess where you are and what it takes to get there.
US-based team · All US citizens · Continental United States only