See how we
actually compare
Compare queues, pricing, access, and control against hyperscalers and GPU clouds.
Side by side
Kracht vs. the field
Representative on-demand figures for comparable GPU classes. Up to 70% cheaper compares Kracht reserved H100 ($2.10/GPU-hr) to hyperscaler on-demand (AWS p5, $12.29/GPU-hr), Jun 2026.
See it for yourself
The same H100, two very different bills
One 8xH100 fine-tune, 10 hours. Drag to compare hyperscaler pricing against a Kracht sample run.
AWS p5 / on-demand
$983
8x H100 / 10 hours
Kracht / on-demand
$231
8x H100 / 10 hours / ~76% less
Same silicon, same speed
You don't trade performance for price
An H100 is an H100. Identical hardware means identical training speed; only the bill changes.
Representative figures / Llama-2-7B LoRA fine-tune / 8x H100 SXM5 / Jun 2026. Throughput is hardware-bound and equal across providers.
The economics
It's not magic. It's the market.
The same H100 costs a fraction here for three structural reasons the hyperscalers cannot easily copy.
Idle supply, not new datacenters
We put GPUs that already exist and sit unused to work, without a giant buildout folded into your hourly rate.
No hyperscaler margin
You pay the market clearing price, not a markup funding managed services your workload never touches.
You pay for compute, nothing else
Per-second metering, zero egress fees, and no minimums keep the bill tied to actual runtime.
Per-provider
The same H100, for less
Competitor on-demand H100 80GB pricing, versus Kracht at $2.10/hr reserved. Our sample on-demand rate is $2.89/hr.
AWS EC2
p5 / H100
Kracht is 17% of their rate
Google Cloud
A3 / H100
Kracht is 19% of their rate
Microsoft Azure
ND H100 v5
Kracht is 19% of their rate
CoreWeave
HGX H100
Kracht is 44% of their rate
Lambda
on-demand H100
Kracht is 70% of their rate
Jarvis Labs
H100 SXM
Kracht is 75% of their rate
No spin
Where the others actually win
We would rather you pick the right tool than oversell ours. Here is where we honestly would not be our own first choice.
A managed everything-suite
If you want GPUs beside 200 first-party managed services, the big clouds win on breadth. We focus on compute, storage, and networking.
A single contractual vendor
Some compliance regimes demand bespoke bare-metal contracts. A marketplace wins on price and flexibility, not owning every layer.
An exotic region, today
We cover 40+ regions, but a very specific jurisdiction may still need a local specialist. We would rather say that clearly.
Still think we're the right fit? Most teams do.
See the real numbers ->Switching
Moving over takes one command
No re-architecting, no lock-in to unwind. Point your existing workload at Kracht and run it the same way, just cheaper.
- Bring your existing Docker image; it runs unchanged
- Same CUDA, drivers, and frameworks you already use
- No egress fees to leave your current cloud
- Keep your tooling: PyTorch, Ray, vLLM, Slurm, k8s
Two sides, one marketplace
Spin up the GPUs you need - or earn from the ones you already own
Rent on-demand compute by the second, or list idle hardware and let it pay for itself. Same marketplace, both directions.
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Product updates and early access to new GPU classes. No spam.