Cloud waste—idle compute, overprovisioned storage, conservative autoscaling settings nobody has revisited since initial deployment—consumes a substantial share of most organizations' cloud budgets, and that share has been trending upward again recently, specifically because AI and GPU workloads introduce cost volatility.
Furthermore, most cost-optimization efforts are one-time projects rather than an ongoing practice. A company does a cleanup pass, captures some savings, and then the waste creeps back within months as new services get deployed without the same scrutiny.
We start with a genuine cost assessment across your infrastructure, identifying real, actionable waste rather than a generic report full of theoretical savings that don't survive contact with your actual constraints.
We cover rightsizing over-provisioned resources, tuning autoscaling to match genuine demand patterns, and specifically for AI workloads, GPU utilization improvement and inference endpoint right-sizing.
System Features
01.Comprehensive Cost Assessment
A genuine audit of compute, storage, and AI/GPU spend, identifying real, actionable waste specific to your actual infrastructure.
02.Rightsizing & Autoscaling Tuning
Compute and storage resources adjusted to match actual demand, with autoscaling calibrated to genuine traffic patterns.
03.AI & GPU Cost Governance
Specific optimization for GPU utilization, inference endpoint sizing, and LLM API token cost — the fastest-growing and hardest-to-forecast cost category.
04.Real-Time Cost Visibility & Alerting
Ongoing monitoring and anomaly alerting on cloud spend, catching cost spikes within minutes rather than discovering them on a monthly bill.
05.Sustained Optimization Discipline
An ongoing practice — not a one-time project — that keeps savings from reversing as infrastructure continues to grow.
AI-Aware Cost Governance
Traditional cloud cost optimization was built around relatively predictable, persistent workloads. AI workloads break that model: GPU training jobs are bursty and expensive, inference endpoints often need to scale from near-zero to significant capacity unpredictably, and LLM API costs scale with token usage in ways that are genuinely hard to forecast without dedicated tracking.
We build cost governance specifically designed around this different economics — treating AI and GPU spend as a distinct cost category requiring its own tagging, allocation, and anomaly detection rather than lumping it into general infrastructure spend where waste becomes invisible.
// Real-World Use Cases
- >Company running AI/ML workloads with unpredictable, hard-to-forecast cloud costs
- >Organization that's done a one-time cost-cutting pass in the past and seen waste creep back afterward
- >Growing business needing genuine cost visibility and allocation across teams and projects
- >Company needing real-time budget alerting to catch cost anomalies before they show up as a shocking monthly bill
- >Business needing a genuine, actionable cost assessment rather than a generic best-practices report
// Measurable Business Impact
- ✔Recovers a meaningful share of cloud spend currently lost to preventable waste
- ✔Brings specific control to the fastest-growing, hardest-to-forecast cost category — AI and GPU workloads
- ✔Catches cost anomalies within minutes through real-time alerting instead of discovering them on a monthly bill
- ✔Establishes an ongoing optimization discipline that prevents savings from reversing over time
- ✔Provides clear cost allocation and visibility supporting better technology investment decisions
Frequently Asked Questions
Find the third of your cloud bill that's waste
Real optimization, real AI cost governance, and the discipline that makes it stick.
Get a cloud cost assessment