TL;DR: The AI Hardware Index now includes cloud GPU providers. Same philosophy as our hardware coverage - transparent pricing, real specifications, side-by-side comparisons. Launch includes 12 providers across managed services and marketplaces, from enterprise H100 clusters to budget RTX 4090s. Here's why this matters and how to use it.
---
Why Cloud, Why Now
Two recent trends pushed this addition to the top of the priority list.
Hardware Costs Are Climbing
As covered in The AI Memory Crisis, HBM shortages are driving unprecedented price increases - DRAM up 50-55% this quarter, server memory projected to surge 60%+. When Micron says they're "sold out for 2026," the calculus for buying vs renting compute shifts.
Higher hardware prices mean longer payback periods. Cloud alternatives that seemed expensive last year look more competitive when the server you wanted costs 20% more than it did in Q4 2025.
TCO Is More Than Sticker Price
The recent 3-Year TCO Breakdown showed that a $289,000 H100 server actually costs $444,564 to operate over three years once power, cooling, and support are factored in. At enterprise scale, operating costs add 54% to the purchase price.
More importantly, utilization rate is the hidden killer. A server running at 25% utilization costs 4x more per useful compute hour than the same server at full load. If workloads are bursty or experimental, cloud often wins.
The Market Is Shifting
These aren't just theoretical observations - the data backs it up:
- GPU cloud is exploding: The GPU-as-a-Service market is projected to grow from $5.79B in 2025 to $49.84B by 2032, according to Fortune Business Insights
- Specialized providers are winning: CoreWeave hit $3.52B revenue in 2025, up 206% year-over-year. Lambda Labs reached a $500M run rate. These GPU-first providers are growing faster than the hyperscalers.
- Cost savings are real: GPU-focused providers offer 50-70% savings compared to AWS, GCP, and Azure for equivalent GPU instances, per Holori's cloud market analysis
- Hybrid is the norm: 68% of US companies running AI in production use a mix of on-premise and cloud infrastructure. IDC predicts 75% hybrid adoption by 2027.
A Lenovo study found that on-premises infrastructure breaks even with cloud at around 12 months for an 8xH100 server - saving $3.4M over five years at sustained utilization. But the key phrase is "sustained utilization." The same study found you need just 5 hours of daily usage to justify on-prem over on-demand cloud. Below that threshold, cloud wins.
This is why having transparent cloud pricing alongside hardware data matters. The right choice depends on your workload profile, not vendor marketing.
What This Category Covers
The new AI Cloud section tracks GPU cloud providers with the same approach applied to hardware:
- Transparent pricing: Actual hourly rates, not "contact sales" placeholders
- Real specifications: GPU model, VRAM, vCPU, system RAM where available
- Side-by-side comparison: Sort and filter across providers
- Clear marketplace labeling: Managed services vs peer-to-peer marketplaces are distinctly marked
Launch Providers
Starting with 12 providers across two categories:
Managed Cloud (10 providers):
| Provider | Regions | Notable Offering |
|---|---|---|
| Lambda Cloud | US | B200 SXM6 from $4.99/hr |
| CoreWeave | US, EU | GB200 NVL72 clusters |
| RunPod | US, EU | Per-second billing, no egress fees |
| Hyperstack | US, EU | H200 SXM from $3.50/hr |
| Nebius | EU, US | 100% renewable, EU data sovereignty |
| Genesis Cloud | EU, US, Canada | Consumer GPUs from $0.08/hr |
| Thunder Compute | US | H100 from $1.89/hr (cheapest on-demand) |
| GMI Cloud | US | No egress fees, NVLink clusters |
| JarvisLabs | India, Finland | Single H200 access, per-minute billing |
| Verda | EU | 100% renewable, formerly DataCrunch |
Marketplace (2 providers):
| Provider | Model | Price Range |
|---|---|---|
| Vast.ai | Peer-to-peer | H100 from $1.50/hr, RTX 4090 from $0.30/hr |
| TensorDock | Marketplace | H100 from $1.91/hr, A100 from $0.67/hr |
Understanding Marketplace Providers
Vast.ai and TensorDock operate differently from managed providers. They're marketplaces where independent hosts set their own prices. This means:
- Lower prices: Competition drives rates down, often 50-80% below managed providers
- Variable reliability: Your workload runs on third-party hardware with varying SLAs
- Price fluctuation: Rates change based on supply and demand
- Security considerations: Hosts have physical access to machines
Marketplace providers are labeled distinctly in the UI, and listings show price ranges rather than fixed rates. They're excellent for non-sensitive workloads, experimentation, and cost-conscious batch processing - but not recommended for production workloads requiring strict SLAs or handling sensitive data.
How to Use This
Compare Across Providers
The AI Cloud listing lets you:
- Filter by GPU model (H100, A100, RTX 4090, etc.)
- Filter by provider or provider type (managed vs marketplace)
- Sort by price to find the cheapest option for a specific GPU
- Compare specs like VRAM, vCPU, and system RAM
Cloud vs Hardware Decision Framework
When does cloud make sense over purchasing hardware?
Cloud typically wins when:
- Utilization will be below 50%
- Workloads are experimental or time-limited
- Capital expenditure isn't available or preferred
- Flexibility to scale up/down matters
- Latest hardware is needed without depreciation risk
Hardware typically wins when:
- Utilization will exceed 70% sustained
- Multi-year workloads with predictable demand
- Data sensitivity requires on-premises control
- Total compute needs are large enough to justify operations overhead
For detailed analysis, see the TCO Breakdown which includes cloud comparison context.
What's Different About Our Coverage
Most cloud comparison sites either:
- Only cover the big three (AWS, GCP, Azure) - missing the specialized GPU cloud providers where pricing is actually competitive
- Require logging in to see prices
- Mix cloud and hardware in confusing ways
The AI Hardware Index approach:
- Specialized providers only: GPU cloud providers focused on AI/ML workloads, not general compute
- Public pricing: If we can't show you the price, we don't list it
- Cloud separate from hardware: Different economics, different comparison. Cloud compares to cloud, hardware to hardware.
- Marketplace transparency: Clear labeling when prices vary by host
What's Next
This is the initial launch. Planned additions:
- More GPU instance types per provider
- Spot/preemptible pricing where available
- Reserved pricing and commitment discounts
- Cloud cost calculator for specific workloads
The goal is the same as hardware: help buyers make informed decisions with transparent data.
---
Explore the new category:
- Browse AI Cloud Providers
- The AI Memory Crisis - Why hardware prices are rising
- 3-Year TCO Breakdown - The real cost of owning AI hardware
---