Is Your AI Infrastructure Bill
Growing Faster Than Your Revenue?
We help Series B+ companies find and eliminate 25–40% in hidden AI compute waste — without slowing down your team or compromising model performance.
The Problem
Your AI Infrastructure Is Burning Cash
Companies at Series B and beyond are spending $200K–$2M+ per month on cloud compute for AI workloads — training runs, inference serving, data pipelines, GPU clusters — with average GPU utilization sitting at just 15%.
25–40% of total cloud spend goes to waste. Your engineering team is optimizing for model performance, not cost efficiency. Your CFO can't forecast AI infrastructure spend with any confidence. And runway burns faster than it should.
15%
Average GPU utilization
25–40%
Of cloud spend wasted
$497B
Projected AI infra market by 2034
The big consultancies address cloud costs generically. FinOps tools provide dashboards but not the architectural judgment to restructure inference pipelines or right-size GPU allocations. Internal teams rarely have the bandwidth or specialized focus to prioritize cost over velocity.
We built Quantific around one thing: making AI infrastructure leaner.
How We Work
Three Phases. Measurable Results.
Each phase reduces your risk while increasing depth. Start with a low-cost diagnostic, move to hands-on implementation, and maintain savings with ongoing advisory.
Phase 1
AI Infrastructure Cost Audit
A diagnostic assessment that analyzes your AI infrastructure spend, identifies waste, and delivers a prioritized savings roadmap with specific dollar estimates.
- •Spend analysis by workload type — training, inference, data processing, storage
- •Top 5-10 optimization opportunities ranked by savings potential
- •Estimated annual savings with confidence ranges
- •Executive summary for board/investor reporting
- •90-day implementation roadmap with ownership assignments
Phase 2
Optimization Implementation
Hands-on engagement working alongside your engineering team to implement the highest-impact optimizations. This is where the largest savings are realized.
- •Inference cost optimization — quantization, vLLM/TensorRT-LLM, batching, autoscaling
- •GPU right-sizing and instance selection across spot, reserved, and on-demand
- •Training pipeline efficiency — checkpointing, data loading, mixed-precision
- •Architecture-level changes — caching, model distillation, workload scheduling
- •Before/after cost comparisons and savings verification
Phase 3
Ongoing Advisory
Monthly retainer providing continuous cost governance, optimization monitoring, and strategic advisory as your AI infrastructure evolves.
- •Monthly infrastructure cost review with executive summary
- •Quarterly deep-dive optimization sprints
- •Real-time advisory on infrastructure decisions and vendor negotiations
- •Annual cloud contract negotiation support
- •Board-ready reporting on AI infrastructure ROI
Why Quantific.AI
Operator Experience, Real-World Results
Built at Hyperscaler Scale
We are operators who have decades of experience building and scaling AI systems at hyperscalers. This isn't theoretical — it's pattern recognition built from hands-on experience managing the exact infrastructure decisions that create million-dollar cloud bills.
Measurable, Quantifiable Outcomes
Every engagement is anchored to a dollar figure on your cloud bill. No abstract strategy decks or vague roadmaps. We deliver a specific savings number, a prioritized implementation plan, and verified cost reductions that show up on your next invoice.
Aligned Incentives Through Value-Based Pricing
We price engagements based on the value created for you, not hours worked. When we earn more, it's because you saved more. This eliminates the perverse incentive of hourly billing and creates a partnership, not a vendor relationship.
Counter-Cyclical Resilience
In growth markets, companies need optimization to maintain margins. In downturns, AI infrastructure is the largest discretionary line item after headcount. We help you manage costs in any economic environment.
Quantific.AI advantage
Typical Approach Vs The Quantific Approach
| Typical Approach | The Quantific.AI approach |
|---|---|
| Broad cloud cost strategy | + AI-specific infrastructure optimization |
| Cost dashboards and alerts | + Architectural judgment with implementation guidance |
| Generic billing optimization | + GPU, inference, and training workload expertise |
| Cost review after the build | + Efficiency designed in from the start |
Who We Work With
Companies with Real AI Workloads
We work with growth-stage and mid-market technology companies that have moved past experimentation. You have models in production, inference endpoints serving real users, and training pipelines running on a cadence. Your AI infrastructure cost is a material line item that the CFO is asking questions about.
Monthly Cloud Spend
$100K – $2M+ with AI as 30–70% of total
Company Stage
Series B through pre-IPO, $20M–$200M+ revenue
Team Size
50–500+ employees, engineering team of 20–200+
The symptoms: cloud bills growing 20–50% quarter-over-quarter with no correlation to revenue growth. GPU utilization below 30%. Engineering teams focused on feature velocity, not cost efficiency. Board pushing for a path to profitability that requires getting infrastructure costs under control.
About
About Quantific.AI
Strategy. Engineering. Intelligence.
Quantific.ai is a specialized advisory practice focused exclusively on helping growth-stage technology companies reduce their AI infrastructure costs. We are not a general cloud consulting firm. We are not a FinOps tool vendor. We are an operator-led practice that understands the infrastructure decisions that lead to million-dollar cloud bills — because we made those decisions.
Stop overpaying for AI infrastructure.
We help Series B+ companies cut AI infrastructure costs by 25–40% without sacrificing model performance. Start with a fixed-fee audit — results in 2–3 weeks.