Selling GPU-hours is a race to the bottom.
H100 rates have collapsed 64–75% in under two years. The differentiator was never the GPU — it's the 85% of the stack above it. Bud Novaria deploys that stack on the infrastructure you already own, in 30 days.
Three forces are closing at once.
Every one of them compresses the same line on your P&L: the price of a GPU-hour.
H100 $8.00 → $2.50–3.50/hr — a fall of 64–75% in under two years. Aggressive providers are already quoting $1.25–1.90. A100 went $3.10 → $1.50, −52%.
300+ new GPU clouds entered the market in 2025. Capacity is not scarce any more; it is inventory.
Headline bare-metal-as-a-service EBITDA reads 57–62%. After depreciation and interest, EBIT lands near 8%. McKinsey's verdict: BMaaS is inherently commoditised.
CoreWeave: $5.13B 2025 revenue on a 4% Q3 operating margin, after an $863M net loss in 2024. Scale does not fix the shape of this business.
AWS, Azure and GCP hold 66% of cloud infrastructure spend — $102.6B a quarter — and are committing $600B+ of combined AI capex in 2026.
Azure AI Foundry deploys 1,900+ models in five clicks, and 65% of Azure customers are already evaluating it. The layer above your GPUs is being sold without you.
95% of GenAI pilots produce zero P&L impact. 42% of companies have abandoned most AI initiatives, up from 17% a year earlier.
Average loss per failed initiative: $7.2M. Your customers are not short of compute. They are short of outcomes.
A $500 billion market is forming above the GPU.
Five tiers. The same racks underneath all of them. Find your rung, then read upward — every step adds revenue per customer, margin, and years of retention.
Every other path costs a year or leaves you renting.
Four ways to get a stack above your GPUs. Only one of them ships this quarter.
| Build in-house | OpenShift / Nutanix / VMware | Neoclouds | Bud Novaria AIOS | |
|---|---|---|---|---|
| Time to production | 18–24 months | 6–12 months | n/a | 30 days |
| Cost | $8–15M+/yr team + $2–5M infra | $0.5–2M licence + $0.9–2.5M staff | — | Revenue share |
| Managed inference | ◐ | ◐ | ✓ | ✓ |
| Managed RAG | ✗ | ✗ | ✗ | ✓ |
| Agent orchestration | ✗ | ✗ | ◐ | ✓ |
| Guardrails | ✗ | ✗ | ✗ | ✓ |
| Air-gapped deployment | ◐ | ◐ | ✗ | ✓ |
| Hardware freedom | ◐ | ✗ | ◐ | ✓ |
| Validated Year-5 revenue | Unknown | GPU-only economics | GPU-only economics | $54.2M |
What Bud puts on your racks.
Seven layers, one deployment. This is the 85% of the stack that sits above the GPU and carries the margin.
Hardware freedom
One control plane across NVIDIA, AMD, Intel, Gaudi and NPUs. You buy on price and availability, not on which vendor your software forces.
CPU-native guardrails
Governance that runs on the CPUs already in your racks — 0.70ms p50, $0.10 per million classifications. No GPU tax on safety.
Demand creation
Bud Studio puts agent-building in the hands of your customers' business teams. Consumption stops depending on their engineers.
Same racks. Same power. A different business on top.
500 H100-equivalents. 50 enterprise customers at the start. $2.85/hr declining 12% a year. Every assumption is on the page.
Even the conservative case is 4.4× the GPU-only revenue — and the GPU-only model never reaches profitability across the five-year curve.
An agent that costs 80% less and answers 3.3× faster.
A global fashion brand's production agent, rebuilt on Bud. The workload did not change; the stack under it did.
Sovereignty is a market, not a feature.
Data residency, air-gap and jurisdiction are requirements a US hyperscaler cannot satisfy by policy. That exclusion is your addressable market.
Four government deployments already shipped on this stack. Sovereign AI Consulting is the highest-margin line on the sheet at 65–80%, and it is the one hyperscalers are structurally excluded from bidding.
Four weeks from rack to revenue line.
Sequence matters. Each week turns on a tier of the ladder — nothing waits on a hardware order.
Put your GPUs on it.
A proof-of-concept on your infrastructure, with your workloads.