Top 10 Neocloud Providers Ranked by Price

Voltage Park rents an H100 for $1.99/hour. CoreWeave charges $6.16 for the same chip. Both are called "neoclouds." Both are renting NVIDIA silicon. The three-times gap between them isn't a pricing mistake, it's a business model showing through.

A neocloud is a GPU-first cloud provider built specifically for AI workloads, unlike AWS, Azure, or GCP, which bolted GPU instances onto infrastructure designed for everything else. The neocloud market is on track to hit $20 billion in revenue in 2026, and every provider on it is making a different bet about who they're selling to: hyperscaler-scale enterprise buyers, research labs training frontier models, or solo builders who need one GPU for an afternoon. That bet shows up directly in the hourly rate.

How These Are Ranked

Ranked by public on-demand H100 pricing where available, cheapest first, since H100 is still the closest thing to a common unit across providers in mid-2026. Where a provider doesn't publish flat H100 rates (Together AI, Fireworks), they're placed by what they actually sell: inference tokens, not raw GPU-hours.

1. Voltage Park — Cheapest Guaranteed Rate

Voltage Park publishes H100s at $1.99/hour, the lowest flat rate of any provider that isn't a peer-to-peer marketplace. Built on a nonprofit-funded GPU fleet, it targets teams that want dedicated (not shared, not spot) capacity without CoreWeave-level pricing. The trade-off is a smaller support and tooling footprint than the bigger names.

2. Nebius — Enterprise Price, European Base

Nebius runs an effective H100 rate around $2.30/hour and picked up a $3 billion deal with Meta in 2026, evidence hyperscalers trust its infrastructure at scale. Spun out of Yandex's cloud division, it's the strongest option for EU-based teams that need data residency without paying CoreWeave's premium.

3. RunPod — Best for Inference Workloads

RunPod prices H100 80GB SXM from $2.49 to $3.49/hour on Secure Cloud, and undercuts everyone on consumer cards through its Community Cloud tier (RTX 3090 from ~$0.19/hour). Its real edge is billing granularity: Pods bill per minute, Serverless bills per second and scales to zero. A production inference API that would run $2,000+/month on an always-on instance elsewhere can cost a fraction of that on RunPod Serverless. See the full RunPod pricing breakdown →

4. Lambda Labs — Best for Sustained Training

Lambda's on-demand H100 SXM5 starts from $2.49/hour, with reserved 1-year and 3-year terms cutting 30 to 50 percent off that rate. Every instance ships with Lambda Stack (PyTorch, CUDA, TensorFlow pre-configured), and clusters scale to 512 H100s over InfiniBand. It bills hourly with no serverless option, so it fits training runs, not bursty inference. Check Lambda Labs plans →

5. Crusoe Cloud — Compute From Stranded Energy

Crusoe publishes H100 at $3.90/hour and built its entire pitch around power sourcing: data centers run on flared natural gas and excess renewable capacity that would otherwise go to waste. The pricing sits mid-pack, but for teams weighing carbon footprint alongside cost, it's the only provider on this list making that its core differentiator.

6. Modal — Serverless GPU for Python

Modal's effective H100 rate runs around $3.95/hour, but the number that matters is billing model, not sticker price. It's built for Python-native serverless compute: functions scale to zero automatically, and you pay per second of actual execution. Best fit is ML engineers who want to deploy a model with a decorator, not provision a VM.

7. Vast.ai — Lowest Possible Floor

Vast.ai is a peer-to-peer GPU marketplace where H100s start around $0.90/hour and range as low as $0.02/hour for older cards, averaging roughly $0.80/hour across all configurations. Pricing is an open bidding system against real-time supply, which means the absolute cheapest compute on this list, and the least predictable reliability. Fine for batch jobs that can tolerate interruption, risky for anything latency-sensitive.

8. Fluidstack — Custom Builds at Frontier Scale

Fluidstack doesn't publish self-serve hourly rates. It operates at the top of the market, building custom AI data centers for buyers who need dedicated capacity at massive scale, most notably a reported $50 billion infrastructure partnership with Anthropic in 2026. Not a fit for solo builders; relevant if you're procuring compute for a frontier lab.

9. Together AI — Compute Plus Inference API

Together AI skips hourly GPU rental for most users and prices by the token instead, positioning itself as compute-plus-inference rather than raw infrastructure. That model suits teams who want an API endpoint for open-source models (Llama, Mixtral, DeepSeek) without managing GPU allocation directly. Dedicated GPU clusters are available for fine-tuning workloads at negotiated rates.

10. CoreWeave — The Enterprise Benchmark

CoreWeave's H100 pricing works out to $6.16/GPU-hour, sold only in 8-GPU HGX bundles, the most expensive on-demand rate on this list and the only provider here that won't sell fewer than 8 GPUs at once. It's also the most validated: publicly traded on Nasdaq since March 2025, and holding a $14.2 billion compute agreement with Meta. You're paying for certainty of supply at hyperscaler scale, not a bargain.

Which Neocloud Should You Actually Rent

If you're running inference or anything bursty, RunPod's per-second billing beats hourly rates from anyone else on this list, even providers with a lower sticker price. If you're training across multiple nodes and can commit to a term, Lambda Labs' reserved discounts and InfiniBand clusters are the stronger buy. If cost is the only variable and reliability doesn't matter, Vast.ai's marketplace floor is unbeatable. Everyone else on this list exists to serve a specific size of buyer between those two extremes.

Check RunPod's current deal → before you spin up your next GPU instance.

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