The cheapest GPU cloud
Compute Cheap provides low-cost GPU compute for training and inference, offering H100 and H200 SXM GPUs as interruptible or reserved capacity with published per-GPU-hour pricing and a simple request/reserve/run workflow.
The cheapest GPU cloud is ai agents software teams evaluate for ai agents. Use this page to review pricing, integration signals, and the best alternatives before you commit.
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Review official source →Quick Overview
Best for: AI Agents
What it does
AI Agents software for decision-makers comparing workflow fit and alternatives.
Best fit
AI Agents
Pricing snapshot
Freemium from $1.15 per GPU-hour (updated Sep 4, 2026)
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Compute Cheap positions itself as a provider of very low-cost GPU compute for training and inference, explicitly offering NVIDIA H100 SXM 80GB and H200 SXM 141GB capacity. It publishes per-GPU-hour prices for interruptible and reserved capacity (for example, H100 interruptible listed at $1.15 per GPU-hour, updated Sep 4, 2026) and advertises being "the world's cheapest GPU compute." The product targets teams running frontier models by providing limited reserved and interruptible capacity in launch sizes from 1 to 8 GPUs, with a minimum reservation of 2,000 GPU-hours and a three-step flow: request capacity, confirm availability with a 50% upfront prepayment, then run workloads.
Affordable H100 and H200 GPU compute for training, inference, and everything in between.
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H100 & H200 GPU options
Offers H100 SXM 80GB and H200 SXM 141GB GPUs for training and inference, listed as available in launch sizes from 1 to 8 GPUs.
Interruptible and Reserved capacity
Two capacity types: interruptible (lower cost, can be reclaimed) and reserved (fixed rate for the full block).
Published per-GPU-hour pricing
Shows explicit hourly rates such as H100 interruptible $1.15/hr, H100 reserved $1.19/hr, H200 interruptible $1.39/hr, and H200 reserved $1.99/hr.
Block reservation workflow
Three-step process: request capacity, confirm availability and prepay 50% to reserve the block, then run workloads against the reserved GPUs.
Minimum reservation
Minimum reservation is 2,000 GPU-hours per request.
Market benchmarking chart
Interactive chart to compare the provider's price against daily market benchmarks across a 31-day window.
Pricing
H100 Interruptible
$1.15 per GPU-hour (updated Sep 4, 2026)- Lower-cost but interruptible (can be reclaimed)
- Launch sizes 1–8 GPUs
H100 Reserved
$1.19 per GPU-hour- Fixed rate for the reserved block
- Minimum reservation 2,000 GPU-hours
H200 Interruptible
$1.39 per GPU-hour- Lower-cost but interruptible
H200 Reserved
$1.99 per GPU-hour- Fixed reserved rate
Use Cases
Training frontier models
Provision H100/H200 capacity for multi-GPU training runs; the product emphasizes usage by teams training frontier models.
Inference and serving
Run inference and serving workloads on H100/H200 GPUs as part of production or research pipelines.
Fault-tolerant or batch training
Use interruptible capacity for cheaper batch or fault-tolerant training jobs that can tolerate preemption.
Integrations
No verified integration details are available.
Benefits
Limitations
Frequently Asked Questions
What's the difference between reserved and interruptible capacity?
Is there a minimum reservation?
How does the 50% deposit work?
What happens if the capacity I asked for isn't available?
Which GPUs can I request?
How is the price calculated?
Getting Started
- 1 Step 1: Request capacity — choose H100 or H200, select interruptible or reserved, and specify GPU-hours (minimum 2,000 GPU-hours).
- 2 Step 2: Confirm availability — Compute Cheap confirms availability for your exact request and you prepay 50% upfront to reserve the block.
- 3 Step 3: Run your workload — use the reserved GPUs to train, serve, and scale; remaining 50% is billed as usage completes.
Support
Contact support via [email protected] (address shown in site footer).
contact page
Site includes a 'Contact' link in the header for additional inquiries.
docs / blog
Blog and pricing posts linked on the site provide pricing history and infrastructure explanations.
API
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